受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国際学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

2件受賞

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：学会誌・学術雑誌による顕彰  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国内学会・会議・シンポジウム等の賞  受賞国：日本国

受賞区分：国際学会・会議・シンポジウム等の賞 

記述言語：英語   掲載種別：研究論文（学術雑誌）  

The end-to-end driving models (E2EDMs) convert environmental information into driving actions using a complex transformation which makes E2EDMs have high prediction accuracy. Due to the black-box nature of transformation, the E2EDMs have low explainability. To solve this problem, explanation methods are used to generate explanations for observation. Based on current explanation methods, previous studies tried to further improve the explainability of E2EDMs by integrating an object detection module, however, these methods have many problems: Firstly, due to the requirement of the object detection module, they lack flexibility. Secondly, they neglect an essential property, i.e. , simplicity, to improve explainability. In this paper, since humans prefer object-level and simple explanations in driving tasks, we argue that explainability is decided by two properties which are the objectification degree (the extent to which driving related-object features are utilized) and simplification degree (the simplicity of the explanation), thus we propose Simplified Objectification Branches (SOB) to improve the explainability of E2EDMs. Firstly, this structure could be integrated into any existing E2EDMs and thus have high flexibility. Secondly, the SOB explicitly improves the simplification degree without sacrificing the objectification degree of the explanations. By designing several indicators, i.e. , heatmap satisfaction, driving action reproduction score, deception level, etc. , we proved that SOB could help E2EDMs generate better explanations. Notably, the SOB could also further enhance E2EDMs’ prediction accuracy.

DOI： 10.1109/TITS.2024.3385754

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Semantic segmentation models comprise an encoder to extract features and a classifier for prediction. However, the learning of the classifier suffers from the ambiguity which is caused by two factors: (1) the weights of a classifier for similar categories may have positive similarities lowing the performance for similar categories, named correlation ambiguity, and (2) the classifier is prone to predict the category with a larger L2 norm and vice versa, termed prior ambiguity. To comedy the issues, we propose Category-Basis Prototype (CBP), frozen and mutually orthogonalized prototypes with equal L2 norm. Orthogonalization prevents the prototypes from being similar to each other and the equality decouples the prediction from the L2 norm. To better shape the feature space, we propose Online Centroid Contrastive Loss (OCCL) equipped with centroid and category- level losses. Experiments show that our method yields compelling results over two widely applied benchmarks indicating the effectiveness of our methods.

DOI： 10.1016/j.patcog.2024.110431

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

歩行者が自車に気づいているかどうかの判断は，安全に車両を運転する上でとても重要である．このような自車への気付きの判断において，歩行者とのアイコンタクトは重要な役割を担っている．従来，このようなアイコンタクト検出には眼球計測により得られる視線推定結果が広く用いられており，道路環境のように歩行者と車両の距離が遠く歩行者の眼球を高解像度に計測できない場合は適用できない．そこで本論文では，歩行者の骨格系列と周囲環境の特徴を統合利用することにより，複雑な交通環境であっても歩行者とのアイコンタクトを精度良く検出可能な手法を提案する．具体的には，歩行者の骨格系列と周囲環境の特徴間の関係性をTransformerベースのモデルで捉えるEye-contact Transformerを構築する．車載カメラ画像群に対してアイコンタクトのアノテーションを付与したデータセットを用いた実験により，提案手法の有効性を確認した．

DOI： 10.14923/transinfj.2023PDP0024

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/ACCESS.2024.3376656

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/ACCESS.2024.3372658

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Deep learning technology has rapidly advanced, leading to the development of End-to-End driving models (E2EDMs) for autonomous vehicles with high prediction accuracy. To comprehend the prediction results of these E2EDMs, one of the most representative explanation methods is attribution-based. There are two kinds of attribution-based explanation methods: pixel-level and object-level. Usually, the heatmaps illustrate the importance of pixels and objects in the prediction results, serving as explanations for E2EDMs. Since there are many attribution-based explanation methods, evaluation methods are proposed to determine which one is better at improving the explainability of E2EDMs.
Fidelity measures the explanation's faithfulness to the model's prediction method, which is a bottommost property. However, no evaluation method could measure the fidelity difference between object-level and pixel-level explanations, making the current evaluation incomplete. In addition, without considering fidelity, previous evaluation methods may advertise manipulative explanations that solely seek human satisfaction (persuasibility). Therefore, we propose an evaluation method that further considers fidelity, our method enables a comprehensive evaluation that proves the object-level explanations genuinely outperform pixel-level explanations in fidelity and persuasibility, thus could better improve the explainability of the E2EDMs.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we present a new task of detecting an object that a target pedestrian is gazing at in a traffic scene called PEdestrian's Gaze Object (PEGO). We argue that the detection of gaze object can provide important information for pedestrian's behavior prediction and can contribute to the realization of automated vehicles. For this task, we construct a dataset of in-vehicle camera images with annotations of the objects that pedestrians are gazing at. Also, we propose a Transformer-based method called PEGO Transformer to solve the PEGO detection task. The PEGO Transformer directly performs gaze object detection with the utilization of whole-body features without a high-resolution head image and a gaze heatmap which the traditional methods rely on. Experimental results showed that the proposed method could estimate pedestrian's gaze object accurately even if various objects exist in the scene.

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Although many advanced works have achieved significant progress for face recognition with deep learning and large-scale face datasets, low-quality face recognition remains a challenging problem in real-word applications, especially for unconstrained surveillance scenes. We propose a texture-guided (TG) transfer learning approach under the knowledge distillation scheme to improve low-quality face recognition performance. Unlike existing methods in which distillation loss is built on forward propagation; e.g., the output logits and intermediate features, in this study, the backward propagation gradient texture is used. More specifically, the gradient texture of low-quality images is forced to be aligned to that of its high-quality counterpart to reduce the feature discrepancy between the high-and low-quality images. Moreover, attention is introduced to derive a soft-attention (SA) version of transfer learning, termed as SA-TG, to focus on informative regions. Experiments on the benchmark low-quality face DB’s TinyFace and QMUL-SurFace confirmed the superiority of the proposed method, especially more than 6.3% Rank1 accuracy improvement is achieved on TinyFace.

DOI： 10.1109/TIP.2023.3335830

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11042-023-15732-z

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/IV55152.2023.10186742

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： 10.14923/transfunj.2022HAP0006

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

With the rapid development of autonomous driving technology, a variety of high-performance end-to-end driving models (E2EDMs) are being proposed. In order to understand the computational methods of E2EDMs, pixel-level explanations methods are used to obtain the explanations of the E2EDMs. However, little attention has been paid to the excellence of the explanations of E2EDMs. Therefore, in order to build trustworthy E2EDMs, we focus on improving the persuasibility of the explanations of E2EDMs. We propose an object-level explanation method (main approach) for E2EDMs, which masks the objects in the image and then treats the change in the prediction result as the importance of the objects, then we explain the E2EDM by the importance of each object. To further validate the effectiveness of object-level explanations, we propose another approach (validation approach), which trains E2EDMs with object information as input and generates the importance of objects using general explanation methods. Both approaches generate object-level explanations, in order to compare these object-level explanations with traditional pixel-level explanations, we propose experimental methods to measure the persuasibility of explanations of E2EDMs through a subjective and objective method. The subjective method evaluates persuasibility based on the extent to which participants think the importance of features indicated by the explanations is correct. The objective method evaluates the persuasibility based on the human annotation similarity between provided with only the important part of images and provided with the complete images. The experimental results show that the object-level explanations are more persuasive than the traditional pixel-level explanations.

DOI： 10.1109/ACCESS.2023.3235739

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We analyze gaze while viewing videos at various playback speeds.
The aims of this paper are to evaluate the comprehension and viewability of videos.
In our experiment, we used 0.75x to 3.0x speed, and measured gaze.
We report differences in gaze behavior among different playback speeds in viewing same video.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We propose a method to detect birds in a 3D environment referring to both audio information observed from a microphone array and visual information observed from a panorama camera. In general, in panorama images, birds appear relatively too small to be detected accurately even with the state-of-the-art deep learning models. Thus, the proposed method takes a two step approach where the birds are first roughly located referring to audio information by Sound Source Localization (SSL), and then image detection is applied within its vicinity. Through evaluation on a dataset annotated with bounding boxes surrounding the birds, we show that the proposed method improves detection performance of birds that appear in relatively small sizes in the image, in both accuracy and processing speed.

DOI： 10.1109/AVSS56176.2022.9959510

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

We have been conducting research focusing on the human gaze as a principal biological reaction while viewing videos. In this paper, we clarify the difference in the area of gaze region under different sound conditions; monaural (1.0 ch), stereo (2.0 ch), and surround (5.1 ch). We measured the gaze of subjects viewing video scenes of natural landscapes and calculated the horizontal and vertical standard deviations of the gaze points and the area of gaze region defined from them. A two-way analysis of variance shows the main effects of each of the sound and scene conditions on the vertical standard deviation of the gaze points and the interaction effect and the main effect of the sound condition on the area of gaze region. In addition, a multiple comparison test shows the significant differences in the vertical standard deviation for the interaction effect between the sound and scene conditions and in the area of gaze region for the sound conditions between monaural and stereo or surround. The hypothesis that the variance of gaze points is expanded by the surround sound is confirmed experimentally.

DOI： 10.11184/his.24.3_195

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

自動車と歩行者が交差するシーンに着目し，車両歩行者間のインタラクティブな行動をモデル化するための2段階入出力隠れマルコフモデル（TS-IOHMM）を提案する．従来の入出力隠れマルコフモデルは，相手の行動に依存して自己の意図と行動が決定される過程をモデル化した．提案するTS-IOHMMは，相手の行動の影響に加え，自己の行動もまた相手の意図に影響を与える点を考慮したモデルで，これまで注目されなかった意図と行動の変化にある法則性（ルール）の獲得を目指したモデルである．検証用に設定したルールに従って意図や行動が変化するシミュレーションデータを作成し，それを用いて提案モデルがどのようなルールを獲得できるか検証する実験を行った．その結果から，提案モデルがシミュレーションと同等の意図や行動を出力する行動ルールを獲得できたことを確認した．

DOI： 10.20729/00218987

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/MIPR54900.2022.00050

記述言語：英語   掲載種別：研究論文（学術雑誌）  

The difficulties in video-based face recognition, such as dramatic pose variations and low quality, can be alleviated by leveraging the rich complementary information between the frames. However, limited by the mini-batch training strategy, the current deep learning methods only utilizes the frames in each batch during training, which ignore the context of the entire video. In this paper, we propose a context-aware feature aggregation scheme to aggregate complementary information between different frames. Firstly, a two-branch structure is designed as the C ontext-aware feature A ggregation N etwork ( CAN ). Secondly, a context-aware training strategy using a context bank is proposed, which alleviates the limitation of mini-batch samples by using the context of the entire video or several images belonging to the same ID and thus achieves global contribution estimation result. Comparative studies on benchmark datasets, such as IJB-C, YouTube Face (YTF), PaSC and COX, confirm that the proposed approach can achieve state-of-the-art level. Meanwhile, qualitative analysis on Multi-PIE dataset indicates that the contribution learned by the CAN is reasonable and beneficial to video face recognition.

DOI： 10.1109/ACCESS.2022.3193787

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

重要な公共交通機関として広く社会に普及している鉄道の沿線には，信号機や踏切など列車の安全運行を支える多くの設備が設置されている．これらの設備の日常的な整備や設置状況などに関する情報収集業務の多くは人手により行われており，その維持管理作業の自動化・効率化を実現する技術が強く求められている．このような課題に対して，営業運転中の列車に搭載したカメラにより前方を撮影した列車前方映像のみを用い，セマンティックセグメンテーションを施すことで鉄道環境における沿線設備などを自動認識する技術に期待が寄せられている．しかし，セマンティックセグメンテーションでは画素単位でクラス情報を人手で付与した学習データが必要であり，高い性能を得るために必要な大量の学習データを用意するコストは非常に高い．そこで本論文では，教師なしデータに対するセマンティックセグメンテーション結果とStructure from Motion（SfM）法による3次元復元結果を組み合わせることによってラベル付き3次元点群を生成し，それらを画像平面に投影することで擬似的なデータ拡張を行うSfM-student法を提案する．これにより，限られたラベルあり学習データのみからセマンティックセグメンテーションの精度向上を図る．実際の鉄道環境で撮影したデータを用いた実験を行ったところ，提案する3次元情報を利用した擬似的なデータ拡張手法は既存のデータ拡張手法と比べてセマンティックセグメンテーション精度を向上させることを確認した．

DOI： 10.14923/transfunj.2021JAP1021

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，電動車いすのより安全な自動運転のための経路計画を目的として，歩行者属性と運転スキルを考慮したリスクポテンシャル関数を運転データから推定する手法を提案し，それらによる歩行者に対する電動車いすの安全マージンの違いを明らかにする．

DOI： 10.14923/transfunj.2021JAL2005

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Face masks bring a new challenge to face recognition systems especially against the background of the COVID-19 pandemic. In this paper, a method mitigating the negative effects of mask defects on face recognition is proposed. Firstly, a low-cost, accurate method of masked face synthesis, i.e. mask transfer, is proposed for data augmentation. Secondly, an attention-aware masked face recognition (AMaskNet) is proposed to improve the performance of masked face recognition, which includes two modules: a feature extractor and a contribution estimator. Therein, the contribution estimator is employed to learn the contribution of the feature elements, thus achieving refined feature representation by simple matrix multiplications. Meanwhile, the end-to-end training strategy is utilized to optimize the entire model. Finally, a mask-aware similarity matching strategy(MS) is taken to improve the performance in the inference stage. The experiments show that the proposed method consistently outperforms on three masked face recognition datasets: RMFRD [1], COX [2] and Public-IvS [3]. Meanwhile, qualitative analysis experiments using CAM [4] indicate that the contribution learned by AMaskNet is more conducive to masked face recognition.

DOI： 10.1109/ACCESS.2022.3150345

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.patrec.2021.07.014

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

For computers to understand human perception, metrics that can capture human perception well are important. However, there are few metrics that characterize the visual perception of humans towards images. Therefore, in this paper, we propose a novel concept and a metric of pointedness of an image, which describes how pointy an image is perceived. The algorithm is inspired by the Features from Accelerated Segment Test (FAST) algorithm for corner detection which looks on the number of continuous neighboring darker pixels surrounding each pixel. We assume that this number would be proportional to the perceived pointedness in the region around the pixel. We evaluated our method towards how well it could capture the human perception of images. To compare the method with similar metrics that describe shapes, we prepared silhouette images of both artificial shapes and natural objects. The results showed that the proposed method gave nearly equivalent perceptual performance to other metrics and also worked in a larger variety of images.

DOI： https://doi.org/10.1007/978-3-030-78635-9_20

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.2493/jjspe.87.440

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： 10.2493/jjspe.87.447

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

Many camera and photo applications, for example image mosaicing, Simultaneous Localization And Mapping (SLAM), and so on, use image keypoints, descriptors, and their matching. Recently, obtaining light-field has become easier thanks to the commercialization of light-field cameras. Light-field is a record of not only light ray intensity but also directions, and multi-view images can be acquired with it. In this paper, a light-field descriptor with light ray directions using light-field is proposed. A light-field descriptor is defined as a set of feature vectors that change according to the direction of the light ray. In addition, a method to match these descriptors is proposed. As the method focuses on the direction of the light ray, it allows more accurate matching than a conventional method when the camera pose changes against the target. To confirm the effectiveness, the proposed method is evaluated by using light-field image recorded in a real scene. As a result, the proposed method is shown to improve the accuraccy over a conventional method. In particular, the closer the distance between the target and the camera, the better accuracy of the proposed method could be achieved.

DOI： 10.2493/jjspe.87.197

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Semantic segmentation has become one of the trending topics in the world of computer vision and deep learning. Recently, due to an increasing demand to solve a semantic segmentation task simultaneously with attribute recognition of objects, a new task named attribute-aware semantic segmentation has been introduced. Since the task requires to handle pixel-wise object class estimation with its attributes such as a pedestrian’s body orientation, previous works had difficulties to handle ambiguous attributes such as body orientations in object-level, especially when segmenting the pedestrians with their attributes correctly. This paper proposes the ColAtt-Net that is an attribute-aware semantic segmentation model augmented by a column-wise mask branch to predict the pedestrians’ orientations in the horizontal perspective of the input image. We firmly assume that the pedestrians captured by a car-mounted camera are distributed horizontally so that for each column of the input image, the pedestrian pixels can be labeled with one orientation uniformly. In the proposed method, we split the output of the base semantic segmentation model into two branches; one branch for segmenting the object categories, while the other one, as the novel column-wise attribute branch, is to map the recognition of pedestrian’s orientations that are distributed horizontally. This method successfully enhances the performance of attribute-aware semantic segmentation by reducing the ambiguity on segmenting the pedestrian’s orientation. Improvements on the pedestrian orientation segmentation are confidently shown by the proposed method in the experimental results, both in quantitative and qualitative views. This paper also discusses how the improved performance becomes an advantage in the autonomous driving system.

DOI： https://doi.org/10.1002/tee.23296

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy using an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we propose Object Manifold Embedding GAN (Ω-GAN) to generate images of variously shaped and arbitrarily posed objects from a noise variable sampled from a distribution defined over the pose and the shape manifolds in a vector space. We introduce Parametric Manifold Sampling to sample noise variables from a distribution over the pose manifold to conditionally generate object images in arbitrary poses by tuning the pose parameter. We also introduce Object Identity Loss for clearly disentangling the pose and shape parameters, which allows us to maintain the shape of the object instance when only the pose parameter is changed. Through evaluation, we confirmed that the proposed Ω-GAN could generate variously shaped object images in arbitrary poses by changing the pose and shape parameters independently. We also introduce an application of the proposed method for object pose estimation, through which we confirmed that the object poses in the generated images are accurate.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we propose an occlusion-robust pose
estimation method of an unknown object instance in an
object category from a depth image. In a cluttered
environment, objects are often occluded mutually. For
estimating the pose of an object in such a situation, a
method that de-occludes the unobservable area of the object
would be effective. However, there are two difficulties;
occlusion causes offset between the actual object center
and the center of its observable area, and different
instances in a category may have different shapes. To cope
with these difficulties, we propose a two-stage
Encoder-Decoder model to extract features with objects
whose centers are aligned to the image center. In the
model, we also propose the Median-shape Reconstructor as
the second stage to absorb shape variations in a category.
By evaluating the method with both a large-scale virtual
dataset and a real dataset, we confirmed the proposed
method achieves good performance on pose estimation of an
occluded object from a depth image.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

Elderly monitoring systems are gaining attention in the modern aging society. For the purpose, Far-InfraRed (FIR) sensors are often used, because they can avoid privacy concerns and are robust to environmental lightings. The authors have previously proposed several methods for human skeleton estimation from an extremely low-resolution FIR image sequence whose resolution is 16 × 16 pixels. For more accurate estimation, this paper proposes a method that is robust to variations of human positions and actions in the FIR sequences. Specifically, to extract features robust to the human positions from the images by using a Convolutional Neural Network (CNN), a global max-pooling layer is inserted into the last layer instead of multiple pooling layers which are not suitable for low-resolution inputs. Also, a network with two branches is introduced that focuses on capturing spatial and temporal information respectively. Moreover, the network has a weighted sum mechanism of their outputs, which depends on the human actions. For evaluation, a dataset was created by capturing action sequences of a human at various positions in the FIR images. Through an experiment, we confirmed that the human motion can be smoothly estimated and that the estimation accuracy is improved by the proposed method.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Imageability is a concept from Psycholinguistics quantizing the human perception of words. However, existing datasets are created through subjective experiments and are thus very small. Therefore, methods to automatically estimate the imageability can be helpful. For an accurate automatic imageability estimation, we extend the idea of a psychological hypothesis called Dual-Coding Theory, that discusses the connection of our perception towards visual information
and language information, and also focus on the relationship between the pronunciation of a word and its imageability. In this research, we propose a method to estimate imageability of words
using both visual and language features extracted from corresponding data. For the estimation, we use visual features extracted from low- and high-level image features, and language features extracted from textual features and phonetic features of words. Evaluations how that our proposed method can estimate imageability more accurately than comparative methods, implying the contribution of each feature to the imageability.

DOI： 10.1145/3372278.3390731

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

It is intuitively obvious that eye-gaze behaviors of experienced drivers are different from those of novice drivers. However, it is not easy to understand the difference in their behavior quantitatively. In this work, we present an explainable eye-gaze behavior modeling method for electric wheelchair drivers based on Inverse Reinforcement Learning (IRL). We first create feature maps that represent risk factors during driving. These feature maps are able to represent not only to what but also from where drivers pay attention. IRL uses the feature maps to learn the reward representing the eye-gaze behaviors and allows us to see important features via the automatic acquisition of the reward. Through analysis of the learned model, we show quantitative evidence that eye-gaze behaviors of experienced drivers are better-balanced by paying attention to multiple risks.

DOI： 10.1109/ITSC45102.2020.9294255

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11042-019-08571-4

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper is an extension of our work in developing an attribute-aware semantic segmentation method which focuses on pedestrian understanding in a traffic scene. Recently, the trending topic of semantic segmentation has been expanded to be able to collaborate with the object’s attributes recognition task; Here, it refers to recognizing a pedestrian’s body orientation. The attribute-aware semantic segmentation can be more beneficial for driver assistance compared to the conventional semantic segmentation because it can provide a more informative output to the system. In this paper, we conduct a study of the data augmentation usage as an effort to enhance the performance of the attribute-aware semantic segmentation task. The experiments show that the proposed method in augmenting the training data is able to improve the model’s performance. We also demonstrate some of qualitative results and discuss the benefits to a driver assistance system.

DOI： 10.1109/ICoICT49345.2020.9166219

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, various facilities have been deployed to support visually impaired people. However, accidents caused by visual disabilities still occur. In this paper, to support the visually impaired people in public areas, we aim to identify whether a pedestrian image sequence obtained by a surveillance camera indicates the presence of a white cane user by analyzing the temporal transition of a human skeleton represented as 2D coordinates. Our previously proposed method aligns the orientation of the human skeletons to various orientations and identifies a white cane user from the corresponding sequences, relying on multiple classifiers related to each orientation. The method employs an exemplar-based approach to perform the alignment. However, it heavily depends on the number of exemplars and consumes excessive memory. In this paper, we propose a method to align 2D human skeleton representation sequence to various orientations using the proposed Skeleton Orientation Alignment Networks (SOANets) based on an encoder-decoder model. Using SOANets, we can obtain 2D skeleton representation sequences aligned to various orientations, extract richer skeleton features, and recognize white cane users accurately. We conducted an experiment to confirm that the
proposed method improves the recognition rate by 16%, compared to the method that does not use the skeleton
orientation alignment.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

高等教育における学習支援システム（LMS: Learning Management System）の重要性は年々高まっており，授業改善や学習分析などさまざまな目的のために活用されている．一方で，数式の記述や作図を必要とする授業や演習，試験など，紙媒体を主体的に扱う教育・学習環境は依然として多く残っており，それらの活動を支援するシステムが強く求められている．そこで本研究では，LMSと連携して手書きレポートを返却するWebサービスの実現を目指し，2016年度から「かみレポ」システムの開発を進めている．本稿では，半年間の試験運用時に実施した手書き数字の認識性能に関する評価と，運用開始後の1年6カ月間の運用評価をもとに，かみレポサービスの有効性と課題，今後の展望についてまとめる．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Surveillance cameras are expected to play a large role in the development of ITS technologies. They can be used to detect abnormally behaving individuals which can then be reported to drivers nearby. There are multiple works that tackle the problem of abnormal behavior detection. However, most of these works make use of appearance features which have redundant information and are susceptible to noise. While there are also works that make use of pose skeleton representation, they do not consider well how to handle cases with occlusions, which can occur due to the simple reason of pedestrian orientation preventing some joints from appearing in the frame clearly. In this paper, we propose a skeleton trajectory representation that enables handling of occlusions. We also propose a framework for pedestrian abnormal behavior detection that uses the proposed representation and detect relatively hard-to-notice anomalies such as drunk walking. The experiments we conducted show that our method outperforms other representation methods.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Recent multimedia applications commonly use text and imagery from Social Media for tasks related to sentiment research. As such, there are various image datasets for sentiment research for popular classification tasks. However, there has been little research regarding the relationship between the sentiment of images and its annotations from a multi-modal standpoint. In this demonstration, we built a tool to visualize psycholinguistic groundings for a sentiment dataset. For each image, individual psycholinguistic ratings are computed from the image's metadata. A sentiment-psycholinguistic spatial embedding is computed to show a clustering of images across different classes close to human perception. Our interactive browsing tool can visualize the data in various ways, highlighting different psycholinguistic groundings with heatmaps.

DOI： 10.3758/s13428-018-1099-3

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

A mimetic word is used to verbally express the manner of a phenomenon intuitively. The Japanese language is known to have a greater number of mimetic words in its vocabulary than most other languages. Especially, since human gaits are one of the most commonly represented behavior by mimetic words in the language, we consider that it should be suitable for labels of fine-grained gait recognition. In addition, Japanese mimetic words have a more decomposable structure than these in other languages such as English. So it is said that they have sound-symbolism and their phonemes are strongly related to the impressions of various phenomena. Thanks to this, native Japanese speakers can express their impressions on them briefly and intuitively using various mimetic words. Our previous work proposed a framework to convert the body-parts movements to an arbitrary mimetic word by a regression model. The framework introduced a phonetic space" based on sound-symbolism, and it enabled fine-grained gait description using the generated mimetic words consisting of an arbitrary combination of phonemes. However, this method did not consider the "naturalness" of the description. Thus, in this paper, we propose an improved mimetic word generation module considering its naturalness, and update the description framework. Here, we define the co-occurrence frequency of phonemes composing a mimetic word as the naturalness. To investigate the co-occurrence frequency, we collected many mimetic words through a subjective experiment. As a result of evaluation experiments, we confirmed that the proposed module could describe gaits with more natural mimetic words while maintaining the description accuracy."

DOI： 10.1007/978-3-030-37734-2_18

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object’s class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task.

DOI： 10.1587/transfun.2019TSP0001

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： https://doi.org/10.2493/jjspe.85.1157

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

近年，自動運転の実用化を目指して，距離センサであるLIDAR（Light Detection and
Ranging）を用いた周辺環境認識に関する研究が活発に行われている．これらの研究
の多くは自動車や歩行者の認識を目的としているが，一般道における自動運転を実用
化するためには，自転車の認識も重要である．自転車は歩行者に比べて移動速度が速
いため，衝突の危険性が高く，安全のために迅速かつ正確な認識が求められる．近年
，3D点群データを直接扱うDNN手法としてPointNetが提案されており，高い物体認識
精度が得られることが示されている．しかし，PointNetは入力点数が固定であるとい
う制約があり，認識対象物体の点数が大きく変化する場合にそのまま適用することは
難しい．そこで本論文では，LIDARにより計測された自転車の認識精度の向上を目的
とし，自転車の点群の分布形状を保持したダウンサンプリングとLIDARの測定精度を
考慮したアップサンプリングを行うサンプリングモジュールを提案する．ダウンサン
プリングでは，点群が低密度な部分の計測点が保存されるようサンプリングを行うこ
とで，自転車の点群の分布形状を保持したサンプリングを可能とする．アップサンプ
リングでは，LIDARの計測時に生じる誤差を考慮し，対象までの距離に応じて点群に
摂動を加えることで，点群の分布形状を大きく変化させることなく点群の密度の増加
を図る．この提案サンプリングモジュールの導入により，PointNetの入力の可変長化
を行う．KITTIデータセットを用いて評価実験を行い，提案手法によって認識精度が向
上することを確認した．

DOI： 10.2493/jjspe.85.1117

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Drivers obtain information on surrounding environment using their eyesights. Experienced eye-gaze behavior is needed when driving at places where multiple risks exist to prepare for and avoid them. In this work, we analyze the change in eye-gaze behavior in such situations while a driver gains experience on the operation of a robotic wheelchair. Accurate distance information in the traffic environment is important to analyze the eye-gaze behavior. However, almost all previous works analyze eye-gaze behavior in a 2D environment, so they could not obtain accurate distance information. For this reason, we analyze eye-gaze behavior in 3D space. Concretely, we developed a novel eye-gaze behavior analysis platform based on a robotic wheelchair and estimated the driver's attention in 3D space. We try to analyze the eye-gaze behavior considering a useful field-of-view in 3D space based on the distance information instead of only the fixation point to investigate the objects that a driver implicitly pays attention to and from where s/he focuses on them. Results show that novice drivers pay attention to a single risk at a time. In contrast, they pay more attention to multiple risks simultaneously as they gain experience. Additionally, we discuss what features are effective to model the eye-gaze behavior based on the results.

DOI： 10.1109/ICCVW.2019.00545

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1587/transinf.2018EDL8219

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Increasingly sophisticated methods for data processing demand knowledge on the semantic relationship between language and vision. New fields of research like Explainable AI demand to step away from black-boxed approaches and understanding how the underlying semantics of data sets and AI models work. Advancements in Psycholinguistics suggest, that there is a relationship from language perception to how language production and sentence creation work. In this paper, a method to measure the visual variety of concepts is proposed to quantify the semantic gap between vision and language. For this, an image corpus is recomposed using ImageNet and Web data. Web-based metrics for measuring the popularity of sub-concepts are used as a weighting to ensure that the image composition in a dataset is as natural as possible. Using clustering methods, a score describing the visual variety of each concept is determined. A crowd-sourced survey is conducted to create ground-truth values applicable for this research. The evaluations show that the recomposed image corpus largely improves the measured variety compared to previous datasets. The results are promising and give additional knowledge about the relationship of language and vision.

DOI： 10.1007/s11042-018-6528-x

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本稿では，過去に別途検出されたことがある人物について，検出した時に得た知識を事前知識として利用することで，その人物を再び検出する際に精度よく検出する「人物再検出」という概念を提案する．また，この概念を車載カメラ映像中の人物検出に適用する方法を示す．本研究では自車がある地点で人物の検出をする際，自車より前にその人物の近くを通過した他車が，先にその場所の近くでその人物を検出している可能性が高いことに着目し，他車による観測を事前知識として人物再検出を行なう．実験により，独自に収集したデータセットを用いた評価において，事前知識を用いることで大きく検出性能が向上することを示す．

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1142/S1793351X19500016

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, the demand for highly accurate pedestrian detectors has increased due to the development of advanced driving support systems.
For the training of an accurate pedestrian detector, it is important to collect a large number of training samples.
To support this, this paper proposes a ``hard negative'' mining method to automatically extract background images which tend to be erroneously detected as pedestrians.
Negative samples are selected based on the assumption that frequent patterns observed multiple times in the same location are most likely parts of the background scene.
As a result of an evaluation using in-vehicle camera images captured along the same route, we confirmed that the proposed method can automatically collect false positive samples accurately.
We also confirmed that a highly accurate detector can be constructed using the additional negative samples.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

3D object pose estimation by using a depth sensor is one of the important tasks in activities by robots. To reduce the pose ambiguity of an estimated object pose, several methods for multiple viewpoint pose estimation have been proposed. However, these methods need to select the viewpoints carefully to obtain better results. If the pose of the target object is ambiguous from the current observation, we could not decide where we should move the sensor to set as the next viewpoint. In this paper, we propose a best next viewpoint recommendation method by minimizing the pose ambiguity of the object by making use of the current pose estimation result as a latent variable. We evaluated viewpoints recommended by the proposed method and confirmed that it helps us to gain better pose estimation results than several comparative methods on a synthetic dataset.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, LIDAR is playing an important role as a sensor for understanding environments of a vehicle’s surroundings. Active-scan LIDAR is being actively developed as a LIDAR that can control the laser irradiation direction arbitrary and rapidly. In comparison with conventional uniform-scan LIDAR (e.g. Velodyne HDL-64e), Active-scan LIDAR enables us to densely scan even distant pedestrians. In addition, if appropriately controlled, this sensor has the potential to reduce unnecessary laser irradiations towards non-target objects. Although there are some preliminary studies on pedestrian scanning strategy for Active-scan LIDARs, in the best of our knowledge, an efficient method has not been realized yet. Therefore, this paper proposes a novel pedestrian scanning method based on orientation aware pedestrian likelihood estimation using the orientation-wise pedestrian’s shape models with local distribution of measured points. To evaluate the effectiveness of the proposed method, we conducted experiments by simulating Active-scan LIDAR using point-clouds from the KITTI dataset. Experimental results showed that the proposed method outperforms conventional methods.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We propose a temporal spotting method of a hand gesture from a low-resolution far-infrared image sequence captured by a far-infrared sensor array. The sensor array captures the spatial distribution of far-infrared intensity as a thermal image by detecting far-infrared waves emitted from heat sources. It is difficult to spot a hand gesture from a sequence of thermal images captured by the sensor due to its low-resolution, heavy noise, and varying duration of the gesture. Therefore, we introduce a voting-based approach to spot the gesture with template matching-based gesture recognition. We confirm the effectiveness of the proposed temporal spotting method in several settings.

DOI： 10.1109/VCIP.2018.8698650

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

The number of food photos posted to the Web has been increasing. Most of the users prefer to post delicious-looking food photos. They, however, do not always look delicious. A previous work proposed a method for estimating the attractiveness of food photos, that is, the degree of how much a food photo looks delicious, as an assistive technology for taking a delicious-looking food photo. This method extracted image features from the entire food photo to evaluate the impression. In our work, we conduct a preference experiment where subjects are asked to compare a pair of food photos and measure their gaze. The proposed method extracts image features from local regions selected based on the gaze information and estimates the attractiveness of a food photo by learning regression parameters. Experimental results showed the effectiveness of extracting image features from outside the gaze regions rather than inside them.

DOI： 10.1109/ISM.2018.00015

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

This paper proposes a Deep Learning-based action recognition method from an extremely low-resolution FIR image sequence. The method recognizes daily actions by humans (e.g. walking, sitting down, standing up, and so on) and abnormal actions (e.g. falling down) without privacy concerns. While privacy concerns can be ignored, it is difficult to compute feature points and to obtain a clear edge of the human body from an extremely low-resolution FIR image. To address these problems, this paper proposes a Deep Learning-based action recognition method whose inputs are the FIR images and their frame differences cropped by the gravity center of human regions.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

In recent years, the demand for pedestrian detection using LIDAR is increasing, as it can be used to prevent traffic accidents involving pedestrians. To avoid traffic accidents, detection of distant pedestrians is very important. However, they are scanned sparsely even if a dense-scan LIDAR is used, and this causes the degradation of the detection accuracy. There-fore, pedestrian detection from sparsely-scanned LIDAR point-clouds is expected to be developed. This paper proposes a LIDAR-based pedestrian detection method using 3DCNN. Since it is difficult to train a 3DCNN directly from sparse point-clouds, the proposed method converts them to a voxel representation using the kernel density estimation based on LIDAR characteristics. To evaluate the performance of the proposed method, an experiment using real-world LIDAR data was conducted. The results showed that the proposed method could detect pedestrians more accurately than detectors trained with other conventional features.

DOI： 10.2493/jjspe.84.1017

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

What target is focused on by many people?
Analysis of the target is a crucial task, especially in a cinema, a stadium, and so on.
However, it is very difficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera.
Therefore, we introduce a new approach for localizing the gaze target focused on by a crowd of people.
The proposed framework aggregates the individually estimated results of each person's gaze.
It enables us to localize the target being focused on by them even though each person's gaze localization from a low-resolution image is inaccurate.
We analyze the effects of an aggregation method on the localization accuracy using images capturing a crowd of people in a tennis stadium under the assumption that all of the people are focusing on the same target, and also investigate the effect of the number of people involved in the aggregation on the localization accuracy.
As a result, the proposed method showed the ability to improve the localization accuracy as it is applied to a larger crowd of people.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we analyze headlight flicker patterns which improve the pedestrian detectability from a driver. Recently, headlights are becoming capable of selectively projecting light on a pedestrian in addition to the normal forward projection. However, it is still not clear how the light should be
projected to effectively improve the visibility of the pedestrian. We actually analyze nine flicker patterns by controlling duty ratios and durations of lighting time, and conduct experiments in field and laboratory settings. As a result, we reveal that a specific fundamental frequency is effective for improving the pedestrian detectability from a driver. We also conclude that the difference between the two settings are not significant.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Nowadays, development of driving support systems and autonomous driving systems have become active.
Pedestrian detection from in-vehicle sensors is one of the most important technologies for these systems.
However, outputs of pedestrian detectors can not be fully trusted in real environments.
Therefore, we propose an estimation system of pedestrian detector's reliabilities for a given scene.
This paper proposes a scene-wise reliability calculation method for LiDAR-based detectors, and a construction method for their estimators.
Here, the problem is how we can define the reliability.
The proposed method defines the reliability considering oversights as the strictest threshold without oversights.
Meanwhile, it defines the reliability considering false detections as the loosest threshold without false detections.
Experimental results showed that the proposed method could properly represent the reliability of a given scene, and estimate their reliability.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Semantic segmentation is an interesting task for many deep learning researchers for scene understanding. However, recognizing details about objects’ attributes can be more informative and also helpful for a better scene understanding in intelligent vehicle use cases. This paper introduces a method for simultaneous semantic segmentation and pedestrian attributes recognition. A modified dataset built on top of the Cityscapes dataset is created by adding attribute classes corresponding to pedestrian orientation attributes. The proposed method extends the SegNet model and is trained by using both the original and the attribute-enriched datasets. Based on an experiment, the proposed attribute-aware semantic segmentation approach shows the ability to slightly improve the performance on the Cityscapes dataset, which is capable of expanding its classes in this case through additional data training.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In order to reduce traffic accidents between a vehicle and a pedestrian, recognition of a pedestrian who has a possibility of collision with a vehicle should be helpful. However, since a pedestrian may suddenly change his/her direction and cross the road, it is difficult to predict his/her behavior directly. Here, we focus on the fact that experienced drivers usually pass by a pedestrian while preparing to step on the brake at any moment when they feel danger. If driver assistant systems can estimate such experienced driver’s decisions, they could early detect the pedestrian in danger of collision. Therefore, we classify the driver’s decisions into three types by referring to the accelerator operation of drivers, and propose a method to estimate the type of the driver’s decision. The drivers are considered to decide their actions focusing on various behaviors and states of a pedestrian, namely pedestrian’s attributes. Since the driver’s decisions change along the timeline, the use of a temporal context is considered to be effective. Thus, in this paper, we propose an estimation method using a recurrent neural network architecture with the pedestrian’s attributes as input. We constructed a dataset collected by experienced drivers in control of the vehicle and evaluated the performance, and then confirmed the effectiveness of the use of pedestrian’s attributes.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： https://doi.org/10.1541/ieejeiss.138.1399

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Nowadays, development of driving support systems and autonomous driving systems have become active.
Vehicle ego-localization using in-vehicle sensors is one of the most important technologies for these systems.
Accordingly, various attempts to localize own vehicle from in-vehicle sensors have been made.
In general, the estimation accuracy of the traveling direction is lower than in the lateral direction.
Therefore, we propose a highly accurate method for ego-localization of the traveling direction based on epipolar geometry using an in-vehicle monocular camera.
The proposed method makes correspondences between in-vehicle camera images and database images with location information, and calculates the location using locations annotated to the corresponding database images.
However, there are many gaps due to the difference of speed and trajectory of vehicles even if the images are obtained along the same road.
To overcome this problem, the distance between the input image and the database image is calculated by the distance metric based on the epipolar geometry and the local feature method.
An experiment was conducted using actual images with correct locations, and we confirmed the effectiveness of the proposed method from its results.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a method to count vehicles from an in-vehicle camera image by regression based on car parts detection. In the case of an in-vehicle camera image, since vehicles are frequently occluded by other vehicles in traffic congestion, it is difficult to accurately count vehicles. Therefore, we propose a method to count vehicles by regression based on the number of visible car parts. For this, we make an estimator by learning the relation between the number of visible car parts and that of vehicles by Support Vector Regression. We evaluated our method using in-vehicle camera images recorded in an actual environment, where the proposed method performed better than counting detected vehicles.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

複数カメラにわたる多人数追跡は，通常，対応付けの候補を減らすために複数のカメラの隣接関係をカメラネットワークというグラフ構造で表現し，カメラネットワーク上の隣接カメラ間での人物対応付けにより実現される．しかし，カメラ間の照明環境の違いや撮影した人物の向きの違いによって対応付けを誤ることがあり，このような場合に追跡が途切れることがある．この問題に対し，隣接カメラだけでなく，カメラネットワーク上で離れたカメラ間での人物対応付けをすることが考えられるが，必要以上に対応付け候補が増加してしまい，誤対応の原因となる．本論文では，本来のカメラネットワークからカメラを無作為に削除したカメラネットワークを複数作る（Camera Dropout）ことで，一部のカメラについては離れたカメラまで考慮した対応付けを行い，更に各カメラネットワーク上での追跡結果をアンサンブルクラスタリングの考え方で統合し，最終的な追跡結果を得るTrajectory Ensembleという手法を提案する．実験では，公開データセットから仮想的に生成した複数のデータセット上で評価を行い，既存の追跡手法に加えてTrajectory Ensembleを導入することにより，精度良く追跡ができることを確認した．

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Group work is widely introduced and practiced as a method to achieve the learning goal efficiently by collaborating group members. However, since most types of group works are carried out in the real environment, it is very difficult to perform formative assessment and real time evaluation without students' feedbacks. Therefore, there is a strong demand to develop a method that supports evaluation of group work. To support evaluation of group work, this paper proposes a method to visualize the real world activity during group work by using first person view cameras and wearable sensors. Here, the proposed method visualizes three scores: (1) individual attention, (2) hand visibility, (3) individual activity. To evaluate the performance and analyze the relationships between scores, we conducted experiments of Marshmallow challenge" that is a collaborative work to construct a tower using marshmallow and spaghetti within a limit of time. Through the experiments, we confirmed that the proposed method has potential to become a evaluation tool for visualizing the activity of the group work."

DOI： 10.1007/978-3-319-91131-1_2

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1587/transinf.2017EDL8215

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

A Learning Management System (LMS) has become a fundamental tool for higher education, and a framework to leverage digital education data in the LMS has attracted attention. On the other hand, there is strong demand to deal with various education data provided not only from electronic media but also non-electronic media, such as a handwritten assignment. To solve this problem, this paper describes the development of 'KamiRepo' system to make it possible to automatically upload handwritten assignments to the LMS. In this system, optical character recognition (OCR) is performed to identify scanned handwritten assignments of individual students and read their scores. Then, their scanned files automatically separated from the entire file of the scanned handwritten assignments are returned to the individual students through LMS together with their corresponding scores. Compared with a conventional system using the dedicated multifunction printer, our developed system is capable of 1) using general-purpose scanners, 2) using a user interface on Web browser, and 3) achieving accurate student identification. We have launched this system in our university in April 2017 and have evaluated its effectiveness. The experimental results using real data collected for 6 months showed that our system achieved 99.7% of success rate in the automatic upload process.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Person re-identification gains an increasing interest
in the surveillance image processing field due to its’ importance
for security. Most approaches to solve the person re-identification
problem match persons one-by-one. However, redundant match-
ing where one of the person is selected for the matching pair
several times often occurs. It also degrades the overall image
matching performance. To overcome the issue, in this paper,
we propose a method which solves the person re-identification
problem for multiple persons simultaneously. Instead of one-
by-one matching, we consider person re-identification as an
instance of the Stable Marriage Problem (SMP). The result of an
experiment showed that the proposed method outperforms some
of the existing state-of-the-art methods applied to the VIPeR
dataset.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ISM.2017.54

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ISM.2017.33

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we report a case study on driver's decision in terms of pedestrian attributes. Among various traffic situations, the situation that a vehicle passes by a pedestrian is one of the major situations. To build a safety driving system that supports a non-experienced driver in such a situation, we analyzed how experienced drivers decide to handle the vehicle in such a situation. Since pedestrian’s behavior can be considered as a key factor for the decision, and also the behavior is different depending on their attributes," such as walking or stopping, noticing the vehicle or not, using a smartphone, etc., we analyzed what pedestrian's attributes affect the driver's decisions. For the analysis, we first built a large-scale dataset of driving data. Using the dataset, we clarified what attributes are dominant for the driver's decision."

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Travel destination recommendation is useful to support travel.Considering the recommendation of regions within the destination area to visit, it could be difficult for the users to explicitly indicate their preference.Therefore, we considered that it would be more intuitive to recommend regions in the destination area that are similar to a region already well-known to the user.Thus, in this paper, we propose a method for the detection of similar geo-regions based on Visual Concepts in social photos.We report experimental results and analyses by applying the proposed method to the YFCC100M dataset.

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/ecj.11987

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a Deep Learning-based action recognition method from an extremely low-resolution thermal image sequence. The method recognizes daily actions by humans (e.g. walking, sitting down, standing up, etc.) and abnormal actions (e.g. falling down) without privacy concerns. While privacy concerns can be ignored, it is difficult to compute feature points and to obtain a clear edge of the human body from an extremely low-resolution thermal image. To address these problems, this paper proposes a Deep Learning-based action recognition method that combines convolution layers and an LSTM layer for learning spatio-temporal representation, whose inputs are the thermal images and their frame differences cropped by the gravity center of human regions. The effectiveness of the proposed method was confirmed through experiments.

DOI： 10.1109/AVSS.2017.8078497

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/3106668.3106670

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Localization along a route is an everyday necessity for in-vehicle navigation systems, and a vital task for automated driving technologies. Visual ego-localization promises reliable accuracy even in challenging urban environments where Global Positioning Systems (GPSs) can fail. Using cameras for localization against a pre-constructed database requires either the creation of a dense three-dimensional feature point map and pose estimation of a query camera relative to this map, or image matching along a database route to determine the capture position of the query camera based on the most similar database image. While the latter method is potentially less computationally intensive and can provide a more compact database, localization accuracy is limited by the discrete positioning information at database frame capture locations. In this paper we propose an image matching method that makes use of image features which are pre-matched during database construction, allowing linear regression coefficients for the relationship between capture position and feature scale to be calculated. The capture position of matched query features can then be estimated to sub-database spacing resolution. By incorporating the visual localization system into a Bayes estimator, we demonstrate an average monocular vision localization accuracy of 0.33 m in tests on actual vehicle image streams.

DOI： 10.1016/j.imavis.2017.07.004

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Surveillance systems play a critical role in security and surveillance. A surveillance system with cameras that work in the visible spectrum is sufficient for most cases. However, problems may arise during the night, or in areas with less than ideal illumination conditions. Cameras with thermal infrared technology can be a better option in these situations since they do not rely on illumination to observe the environment. Furthermore, in our daily lives, it is common for humans to wear headgears such as glasses, masks, and hats. In surveillance, such headgears can be a hindrance to the identification of a person, and hence pose a certain degree of risk. This is not ideal in areas where the identity of a person is important, for example, in a bank. Therefore, in this paper we propose a headgear recognition method using an innovative decomposition approach on thermal infrared images. The decomposition method is based on Robust Principal Component Analysis, a modification of the popular Principal Component Analysis. The proposed method performs decomposition on a human image and isolates headgears in the image for recognition purposes. Experiments were conducted to evaluate the capability of the proposed method. The results show a positive outcome when compared with other methods.

DOI： 10.1109/QIR.2017.8168475

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Multiple person tracking over a camera network is usually performed by matching person images between adjacent cameras. It easily fails by a temporal appearance change of the persons caused by environmental illumination and observation orientation of a camera. To solve this problem, matching person images across not only adjacent cameras but also cameras multiple hops away in the camera network is effective, but such relaxation of spatiotemporal cues also cause tracking failure due to the increase of matching candidates. To avoid the failure, we introduce Random Camera Drop" to generate different camera networks which relax the spatio-temporal cues partially and randomly. We then, integrate tracking results over the networks to a consensus tracking result by a novel concept "Trajectory Ensemble", an extension of unsupervised ensemble learning for the multiple person tracking over a camera network problem. We evaluated the framework on several virtual datasets generated from a public dataset, "Shinpuhkan 2014 dataset" and confirmed that the proposed method achieves the highest tracking results among several comparative methods."

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We introduce a method that uses a single camera to localize a vehicle within a pre-constructed map consisting of a voxel occupancy grid and road-line marker positions. Sophisticated mapping hardware is capable of creating high accuracy 3D maps of road environments, but localizing a vehicle within such maps is one of the challenges at the forefront of automated driving. A solution which is robust to dynamic environments, while using only inexpensive sensors, is a difficult problem. In addition, maps that enable precise localization consume a lot of data which is impractical for the expansive environments encountered in real-world road networks. We show how using the area of edge regions shared between rendered views of a compact voxel map and in-vehicle camera images can be coupled with non-linear optimization methods to determine the camera position and pose.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/VR.2017.7892257

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.5220/0006101201730178

記述言語：英語   掲載種別：研究論文（学術雑誌）  

This paper addresses the attribute recognition problem, a
field of research that is dominated by studies in the visible spectrum. Only
a few works are available in the thermal spectrum, which is fundamentally
different from the visible one. This research performs recognition specif-
ically on wearable attributes, such as glasses and masks. Usually these
attributes are relatively small in size when compared with the human body,
on top of a large intra-class variation of the human body itself, therefore
recognizing them is not an easy task. Our method utilizes a decomposi-
tion framework based on Robust Principal Component Analysis (RPCA) to
extract the attribute information for recognition. However, because it is dif-
ficult to separate the body and the attributes without any prior knowledge,
noise is also extracted along with attributes, hampering the recognition
capability. We made use of prior knowledge; namely the location where
the attribute is likely to be present. The knowledge is referred to as the
Probability Map, incorporated as a weight in the decomposition by RPCA.
Using the Probability Map, we achieve an attribute-wise decomposition.
The results show a significant improvement with this approach compared
to the baseline, and the proposed method achieved the highest performance
in average with a 0:83 F-score.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.5220/0006100901570164

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.5220/0006101101650172

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.5220/0006229306110616

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal.
In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database.
Image feature points are extracted, but the calculation of camera poses is not required---instead we make use of the feature points' scale.
For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into ``tracklets''. These ``Feature scale tracklets'' are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations.

DOI： 10.1587/transfun.E100.A.702

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本研究では，調理者の視線遷移と瞬きの時系列パターンに基づいて調理動作を識別する手法を提案する．視線情報が人間の行動を理解するうえで重要であることから，頭部に装着した視線計測装置により視線運動データを計測し，調理動作識別に用いる．視線パターンの表現には，視線情報に着目した動作認識において有用とされるN-gramを採用する．従来研究では，前フレームからの相対的な視線遷移のみがN-gramを構成する記号に用いられていた．これに対して本研究では，調理動作には調理対象への注視を持続させる振る舞いが頻繁に表れることから「停留」と，動作に対する集中を考慮するために「瞬き」を表現する記号を加えた．そして，高次元特徴を効率的に学習でき，特徴量のノイズに強いとされるRandom Forestを利用し，視線パターンが各調理動作クラスに属する事後確率を出力する識別器を構築した．実験結果から各調理動作の性質と識別に対する寄与度が高い視線パターンとの関係性を明らかにしたうえで，調理動作識別において従来手法よりも高い識別精度が得られ，提案手法の有効性を確認した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we propose a multiclass classifier training method which reduces fatal" misclassifications by cost-relaxation of "tolerable" misclassifications in one-against-all classifiers training, named misclassification tolerable learning. In a binary classifier in the one-against-all classifiers, we introduce a new class group "conceptually similar classes," whose class labels are similar to the positive class. In the case of pedestrian orientation classification, the conceptually similar classes are defined as neighboring orientations to the positive orientation. We consider the misclassification of the conceptually similar classes to the positive class as tolerable misclassification. By relaxing the cost of the tolerable misclassifications, our proposed classification method reduces fatal misclassifications of non-similar classes. We evaluated the cost-relaxation effectiveness on several public datasets and confirmed that the proposed method outperforms the normal SVM on all of the datasets in the soft criterion by achieving 78.63% recognition rate on PDC Dataset."

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

In recent years, demands for Advanced Driving Assistance Systems (ADAS) is increasing, and pedestrian detection has become one of the most important and popular technologies in this system. In the case of pedestrian detection using an in-vehicle camera, since the road environment varies widely according to difference in lightning, weather, etc., it is very difficult to handle them with a single classifier, and numerous false positives are detected. To overcome this problem, this paper proposes a novel pedestrian detection method by scene adaptation based on false positive mining. When we observe the appearance of false positives in in-vehicle camera images, those with similar features are found even in different road environments. The proposed method focuses on the appearance of the detected false positives, and considers it as a scene that the classifier is not good at. By analyzing such a false positive tendency in each scene, the proposed method associates the false positive tendency to each scene and then associates them to each training image. Then, classifiers are constructed so that they can cope with false positives observed in each scene. To evaluate the effectiveness of the proposed method, experiments were conducted on the Caltech Pedestrian Detection Benchmark datasets. Its results showed that the proposed method outperforms the method without adaptation.

DOI： 10.2493/jjspe.82.1085

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Pedestrians texting-while-walking" increase the risk of traffic accidents, since they are often not paying attention to their surrounding environments and fails to notice approaching vehicles. Thus, the recognition of texting-while-walking from an in-vehicle camera should be helpful for safety driving assistance. In this paper, we propose a method to recognize a pedestrian texting-while-walking from in-vehicle camera images. The proposed approach focuses on the characteristic relationship between the arm and the head poses observed during a texting-while-walking behavior. In this paper, Pose-Dependent Joint HOG feature is proposed as a novel feature, which uses parts locations as prior knowledge and describes the cooccurrence of the arm and the head poses. To show the effectiveness of the proposed method, we constructed a dataset and evaluated it."

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a method for detecting obstacles using train frontal view videos.
In recent years, obstacles detection using in-vehicle camera is actively developed for satisfying demands from various applications.
In the field of obstacle detection, most methods employ machine learning approach, and they can detect only trained obstacles, such as pedestrian, bicycle, etc.
Therefore, they cannot detect untrained general obstacles.
To overcome this problem, this paper propose a background subtraction method using moving camera.
The proposed method first computes frame-by-frame correspondences between the present and the database train frontal view image sequences, and detects obstacles by applying image subtraction to corresponding frames.
To confirm the effectiveness of the proposed method, experiments were conducted by using several image sequences captured at the experimental railroad track.
The experimental result showed that the proposed method could detect various obstacles accurately and effectively.

DOI： 10.1109/ICIP.2016.7533104

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Collaborative learning is efficient teaching/learning method, and it is widely introduced and practiced in various situations. However, it has a difficulty to perform formative assessment and real time evaluation without students' feedbacks. Therefore, demand for technologies to support formative assessment in collaborative learning is increasing. To tackle this problem, we have started the research project for automatic sensing and visualization of real world activities in collaborative learning. In this paper, we will report details about preliminary group work experiments and its results with visualization tool.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

It is becoming important for working robots to be able to identify and pick objects in various tasks. As in the recent Amazon Picking Challenge, using a marker for the picking task is a more practicable approach. However, a common marker code for working robots does not exist so far. Conventional marker codes as represented by QR code or ARToolKit marker cannot be reliably detected from various viewpoints. Thus in this paper, we propose a new encoded marker which is flexibile to the marker's positions and blur. The proposed marker can be detected by an approach based on the scale space theory independent from such conditions. In addition, the representation of data by M-sequence makes the encoded marker robust to blur. Experimental results showed the effectiveness of the proposed marker compared to the ARToolKit marker. Since the marker is more robust against ground clutter noise, various positions of markers and blur, it is more practicable.

DOI： 10.1541/ieejeiss.136.1367

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, pedestrian detection from an invehicle camera has been attracting attention.However, in the case of a raining situation, the detection accuracy decreases because the head of a pedestrian tends to be occluded by an umbrella. In oder to handle such cases, in this paper, as a variation of the Deformable Part Model (DPM) which is widely used in the field of object recognition, we propose Parts Selective DPM (PS-DPM)" which selectively chooses the original part filters and additional part filters trained independently. In the detection of pedestrians possessing an umbrella, the selection of head and umbrella parts will make pedestrian detection more robust to the occlusion. We conducted experiments to evaluate the performance of the proposed method. As a result, pedestrian detection with the proposed PS-DPM achieved high detection accuracy in rainy weather, compared with the detection by the conventional DPM. Moreover, we confirmed that it did not decrease the pedestrian detection accuracy in fine weather."

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Human body segmentation has many applications in a wide
variety of image processing tasks, from intelligent vehicles to entertainment.
A substantial amount of research has been done in the field of segmentation
and it is still one of the active research areas, resulting in introduction of
many innovative methods in literature. Still, until today, a method that can
overcome the human segmentation problems and adapt itself to different
kinds of situations, has not been introduced. Many of methods today try to
use the graph-cut framework to solve the segmentation problem. Although
powerful, these methods rely on a distance penalty term (intensity difference
or RGB color distance). This term does not always lead to a good separation
between two regions. For example, if two regions are close in color, even
if they belong to two different objects, they will be grouped together, which
is not acceptable. Also, if one object has multiple parts with different
colors, e.g. humans wear various clothes with different colors and patterns,
each part will be segmented separately. Although this can be overcome
by multiple inputs from user, the inherent problem would not be solved.
In this paper, we have considered solving the problem by making use of a
human probability map, super-pixels and Grab-cut framework. Using this
map relives us from the need for matching the model to the actual body,
thus helps to improve the segmentation accuracy. As a result, not only the
accuracy has improved, it also became comparable to the state-of-the-art
interactive methods.

DOI： 10.1587/transfun.E99.A.943

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a method for estimating the attractiveness of food photos in order to assist a user to shoot them attractively.
The proposed method extracts both color and shape features from input food images, and then integrates them according to a regression scheme.
By this way, the proposed method estimates the attractiveness of an unknown food photo.
We also created a food image dataset taken from various 3D-angles for each food category, and set target values of their attractiveness through subjective experiments.
Then, we evaluated the performance of the proposed method in two different ways of constructing the attractiveness estimator: One that constructs it for each food category, and the other that constructs a common attractiveness estimator for all food categories.
Experimental results showed the effectiveness of the proposed method in addition to the necessity for adaptively selecting the estimator depending on the appearance of foods for further performance improvement.

記述言語：英語   掲載種別：研究論文（学術雑誌）  

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations.

DOI： 10.3390/s16040568

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

This paper proposes a method for detecting general obstacles on a road by subtracting a present in-vehicle camera image sequence from its corresponding past image sequences using an illumination insensitive feature.
We conducted experiments using several image sequences captured under various illumination conditions, and confirmed the effectiveness of the proposed method.

DOI： 10.1541/ieejeiss.136.588

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/2857491.2857500

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.5220/0005718105450551

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.5220/0005668702730279

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper addresses an attribute recognition problem in thermal images, specifically on worn objects such as hat and glasses. Although attribute recognition is a growing research field, there are not much work done in thermal infrared spectrum. In this spectrum, since illumination is not a problem, it could be a better option to be used in nighttime or poorly lit areas. The proposed method uses only the attribute information and excludes the unnecessary information for the recognition. To achieve this, we propose attribute recognition based on feature decomposition using Robust Principal Component Analysis (RPCA). An experiment to evaluate the capability of the proposed method was conducted on the dataset created for this research. The results show that the proposed method outperformed the method without decomposition by 14% in average with a maximum of 27% increase in a specific attribute.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Segmentation is one of active areas in computer vision field with application in many areas from entertainment to intelligent vehicles (IVs). Among the objects, humans themselves have always been among the most interested subjects because of
their special features. Since human body has an articulated structure, modeling and recognizing different variations in the body has proved to be very difficult. Wearing various kinds of clothes in different situations which can have a completely dissimilar appearance based on the clothing type, makes the modeling much more difficult. Add to this, the common problems of vision like illumination changes, blurring due to camera movements, etc. make the problem even more difficult. Thus having a system that can segment human subjects accurately can be useful in many applications. In this paper, we propose a system for segmenting human subjects using Statistical Shapes Models (SSM) feedback and a texture aware version of Grab-cut which incorporates texture feature for improving the segmentation accuracy. Our experiments show that the proposed system has an acceptable accuracy compared to the state-of-the art interactive methods and much better than the conventional ones.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： 10.14923/transinfj.2015JDP7031

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： 10.14923/transinfj.2015HAP0015

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Vehicle ego-localization is a critical task not only for in-car navigation systems, but also for emerging intelligent and autonomous vehicle technologies. Visual localization methods that determine current location by performing image matching against a pre-constructed database have an accuracy limited by the spatial distance between database images. In this paper we propose a method that uses the scale of feature points to interpolate the position of the query image between two database images. We show how this simple contribution offers an appreciable improvement in localization accuracy with an extremely minimal increase in processing time, especially when used in conjunction with image matching methods that already monitor feature scale. Our experiments showed an increase of up to 33\% in average localization accuracy when compared to a method without any interpolation.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/2810188.2810189

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ITSC.2015.200

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Recently, the number of cooking recipes on the Web is increasing.
However, it is difficult to search them by tastes or textures although
they are actually important considering the nature of the contents.
Therefore, we propose a method for estimating the tastes and the
textures of a cooking recipe by analyzing them.
Concretely, the proposed method refers to an ingredients feature from
the ``ingredients list'' and image features from the ``food image'' in
a cooking recipe.
We confirmed the effectiveness of the proposed method through an
experiment.

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1541/ieejeiss.135.1000

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

As the number of cooking recipes posted on the Web increases, it becomes difficult to find a cooking recipe that a user needs. Moreover, even if it can be done, it is still difficult for users to arrange the cooking recipe, for example, by replacing ingredients with different ones. To deal with such problems, we propose a framework for typicality analysis of the combination of ngredients. The framework calculates a typicality value for each combination of ingredients. The list of ingredients can be arranged by adjusting the typicality value by adding or removing ingredients iteratively. The effectiveness of the proposed framework was confirmed through subjective experiments.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Web based translation support systems such as Crowdin and Transifex make it easy for regional Sakai communities to collaborate globally in translation. In the fall and winter of 2014, Spanish Sakai community and Japanese Sakai community are collaborating in translation of Sakai 10 using Transifex as a common translation platform. This collaboration brought a lot of things to two communities. For instance, to develop a tool to import resource bundle files to Transifex in a specific manner, we can have an ease of use platform to translate modular designed software such as Sakai. And this platform will be extended for every regional community and for every project in Apereo community.
During this session we will talk about followings;
(1) Overview
(2) Benefits for Apereo community
(3) Context dependent translation by gettext Portable Object
(4) Community translation strategy in Sakai Spanish Users (S2U)
(5) Automatized process by Jenkins

記述言語：英語   掲載種別：研究論文（学術雑誌）  

In this paper, as a tool for structuring a large volume of archived news videos according to their semantic contents, we propose a method that effectively detects scene duplicates, assuming the presence of a person speaking in the videos. A scene duplicate is defined here as a pair of video segments taken at the same event from different viewpoints. When considering scenes where a subject is speaking in news videos, referring to the audio channel could be effective to detect scene duplicates regardless of viewpoints. However, it cannot be relied on when external audio sources overlap the original one or when the subject is actually not speaking. In contrast, the image channel can be useful in most cases. However, significant difference in viewpoints could prevent accurate detection. Therefore, we propose a method that combines the results obtained from both audio and image channels in order to improve the accuracy of scene duplicate detection from news videos. The proposed method was evaluated through an experiment with actual broadcast news videos by comparing it with a conventional method. As a result, we confirmed that the detection accuracy significantly improved by the proposed method in both recall and precision.

DOI： 10.1142/S1793351X15400048

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

大学教育で利用されているオープンソースソフトウェアの多くは英語圏で開発されているため日本語への翻訳が必要となる．今後大学では複数のシステムにより教育支援のためのIT環境を構築することが予想されるが，システム間で用語が異なっているとユーザに違和感を抱かせる．この課題に対して，大学教育で頻出する用語および用例を包含する大規模な共通翻訳メモリを作成することによって一貫性のある翻訳が実現できると考えた．また，翻訳メモリによる翻訳では実現できないコンテキスト依存の翻訳を可能とするgettext形式のPOファイルを併用することにより，Javaで実装されたSakaiとPHPで実装されたMoodleおよびMaharaをクラウドベースの翻訳支援システムであるTransifexを用いて同一のプロセスで翻訳した．本稿では大学教育用オープンソースソフトウェアに対して一貫性のある翻訳を目指して行ったプロジェクトについて述べる．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, image inpainting is widely used to remove undesired objects from an image. Especially, the removal of temporal objects, such as pedestrians and vehicles, in street-view databases such as Google Street View has many applications in Intelligent Transportation Systems (ITS). To remove temporal objects, Uchiyama et al. proposed a method that combined multiple image sequences captured along the same route. However, when spatial alignment inside an image group does not work well, the quality of the output image of this method is often affected. For example, large temporal objects existing in only one image create regions that do not correspond to other images in the group, and the image created from aligned images becomes distorted. One solution to this problem is to select adaptively the reference image containing only small temporal objects for spatial alignment. Therefore, this paper proposes a method to remove temporal objects by integration of multiple image sequences with an adaptive reference image selection mechanism.

DOI： 10.5220/0005357102330239

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Estimation of human orientation contributes to improving the accuracy of human behavior recognition. However,
estimation of human orientation is a challenging task because of the variable appearance of the human
body. The wide variety of poses, sizes and clothes combined with a complicated background degrades the
estimation accuracy. Therefore, we propose a method for estimating human orientation using coaxial RGBDepth
images. This paper proposes Depth Weighted Histogram of Oriented Gradients (DWHOG) feature
calculated from RGB and depth images. By using a depth image, the outline of a human body and the texture
of a background can be easily distinguished. In the proposed method, a region having a large depth gradient
is given a large weight. Therefore, features at the outline of the human body are enhanced, allowing robust
estimation even with complex backgrounds. In order to combine RGB and depth images, we utilize a newly
available single-chip RGB-ToF camera, which can capture both RGB and depth images taken along the same
optical axis. We experimentally confirmed that the proposed method can estimate human orientation robustly
to complex backgrounds, compared to a method using conventional HOG features.

DOI： 10.5220/0005305301130120

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ISM.2014.43

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Accurate vehicle positioning is important not only for in-car navigation systems but is also a requirement for emerging autonomous driving methods. Consumer level GPS are inaccurate in a number of driving environments such as in tunnels or areas where tall buildings cause satellite shadowing. Current vision-based methods typically rely on the integration of multiple sensors or fundamental matrix calculation which can be unstable when the baseline is small.

In this paper we present a novel visual localization method which uses a visual street map and extracted SURF image features. By monitoring the difference in scale of features matched between input images and the visual street map within a Dynamic Time Warping framework, stable localization in the direction of motion is achieved without calculation of the fundamental or essential matrices.

We present the system performance in real traffic environments. By comparing localization results with a high accuracy GPS ground truth, we demonstrate that accurate vehicle positioning is achieved.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Crowd analysis using cameras has attracted much attention for public safety and marketing. Among techniques of the crowd analysis, we focus on spatial people density estimation which estimates the number of people for each small area in a floor region. However, spatial people density cannot be estimated accurately for an area far from the camera because of the occlusion by people in a closer area. Therefore, we propose a method using a memory based regression method with images captured from cameras from multiple viewpoints. This method is realized by looking up a table that consists of correspondences between people density maps and crowd appearances. Since the crowd appearances include situations where various occlusions occur, an estimation robust to occlusion should be realized. In an experiment, we examined the effectiveness of the proposed method.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Vehicle ego-localization is an essential process for many driver assistance and autonomous driving systems. The traditional solution of GPS localization is often unreliable in urban environments where tall buildings can cause shadowing of the satellite signal and multipath propagation. Typical visual feature based localization methods rely on calculation of the fundamental matrix which can be unstable when the baseline is small.
In this paper we propose a novel method which uses the scale of matched SURF image features and Dynamic Time Warping to perform stable localization. By comparing SURF feature scales between input images and a pre-constructed database, stable localization is achieved without the need to calculate the fundamental matrix. In addition, 3D information is added to the database feature points in order to perform lateral localization, and therefore lane recognition.
From experimental data captured from real traffic environments, we show how the proposed system can provide high localization accuracy relative to an image database, and can also perform lateral localization to recognize the vehicle's current lane.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a camera-based visibility estimation method for a traffic sign.
The visibility here indicates how a visual target is easy to be detected and recognized by a human driver (not a machine).
This research aims at realizing a nuisance-free driver assistance system which sorts out information depending on the visibility of a visual target, in order to prevent driver distraction.
Our previous study on estimating the visibility of a traffic sign considered only the effect of the local region around a target, assuming the situation that a driver's gaze is around it.
The proposed method integrates both the local features and global features in a driving environment without such an assumption.
The global features evaluate the positional relationships between traffic signs and the appearance around the fixation point of a driver's gaze, which considers the effect of the driver's entire field of view.
Experimental results showed the effectiveness of incorporating the global features for estimating the visibility of a traffic sign.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

We propose an accurate ego-localization method by searching a streetview database composed of single front-view in-vehicle camera images. Previously, we proposed an image distance metric reflecting the positional relation of two cameras based on epipolar geometry analysis, and used it for ego-localization. However, since the method employed a dynamic time warping strategy to avoid the effect from outliers, both input and database images needed to be image sequences. To overcome this problem, the method proposed in this paper reformulates the previous image distance metric to a novel image distance that requires only a single in-vehicle camera image as an input, which is realized by considering the sequential property of the images in the database. We conducted experiments using multiple image sequences captured under various conditions by using an in-vehicle camera mounted on the windshield of a car. The experimental results showed that the proposed method could achieve an accuracy of 89%, and we confirmed its effectiveness.

DOI： 10.1541/ieejeiss.134.702

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

It is important to track the flow of topics to thoroughly understand the contents. Accordingly, a method that structures the chronological semantic relations between news stories, namely a 'topic thread structure' has been proposed.It allows the comprehensive understanding of a topic by chronologically tracking stories one by one from the initial story. However, this task imposes a user to watch many stories when it contains various sub-topics. Thus, we propose method that estimates the representative story transition in a topic thread structure. In the proposed method, features obtained from a story and those from the topic thread structure are used for the estimation. We confirmed the effectiveness of the proposed method by comparing the results obtained from the proposed method to the ground truth obtained from votes in a subjective experiment. However, this task imposes a user to watch many stories when it contains various sub-topics. Thus, we propose method that estimates the representative story transition in a topic thread structure. In the proposed method, features obtained from a story and those from the topic thread structure are used for the estimation. We confirmed the effectiveness of the proposed method by comparing the results obtained from the proposed method to the ground truth obtained from votes in a subjective experiment

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper discusses a novel sensing strategy to retrieve real-world information. Instead of using sensor devices such as cameras, microphones and so on, the proposed approach involves people in real-world sensing to acquire requested information as accurately and quickly as possible. We call the proposed sensing strategy as Human Cloud Sensing (HCS)". In this paper, we introduce the concept of HCS and report some experimental results which were conducted to investigate the feasibility of "Can a human be a sensor?"."

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, accurate pedestrian detection from in-vehicle camera images is focused to develop a safety driving assistance system. Currently, successful methods are based on statistical learning. However, in such methods, it is necessary to prepare a large amount of training images. Thus, the decrease in the number of training images degrades the detection accuracy. That is, in driving environments with few or no training images, it is difficult to detect pedestrians accurately. Therefore, we propose an approach that collects training images automatically to build classifiers for various driving environments. This is expected to realize highly accurate pedestrian detection by using an appropriate classifier corresponding to the current location. The proposed method consists of three steps; Classification of driving scenes, collection of non-pedestrian images and training of classifiers for each scene class, and associating a scene-class-specific classifier with GPS location information. Through experiments, we confirmed the effectiveness of the method compared to baseline methods.

DOI： 10.5220/0004677003540361

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In this paper, we propose an exemplar-based super-resolution method applied to a human body in a surveillance video. Since persons are usually captured as low-resolution images by a video surveillance system, it is sometimes necessary to perform detection and identification of persons from not only a human face but also from the human body appearance. The super-resolution for a human body image is difficult because the appearances of person images vary according to the color of clothing and the posture of persons. Thus, we focus on the high-frequency components that could restore the lost high-frequency components of the low-resolution image regardless to the variation of the clothing. Therefore, the purpose of the work presented in this paper is to apply the exemplar-based super-resolution using high-frequency components for a low-resolution human body image to generate a high-resolution human body image so that both computer systems and humans can identify persons more accurately. As a result of experiments, we confirmed the effectiveness of the proposed super-resolution method.

DOI： 10.5220/0004686101150121

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/2506023.2506028

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In recent years, advances in pedestrian detection technology have resulted in the development of driving assistance systems that notify the drivers of the presence of pedestrians. However, warning of all of the presence of pedestrians would confuse the driver. Therefore, the driver should only be notified of the less detectable pedestrians to avoid confusion. To achieve this, it is necessary to develop a method to predict the driver’s perception performance of pedestrian detectability. This paper proposes a method that predicts the pedestrian detectability considering the difference between individual drivers. The proposed method constructs a predictor specific to each driver, in order to predict the pedestrian detectability precisely. To obtain the ground truth of the pedestrian detectability, we conducted an experiment by human subjects using images from an in-vehicle camera including pedestrians. From the comparison between the output of the proposed method and the actual detectability, we confirmed that the proposed method significantly reduces the prediction error in comparison with the existing methods.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Recently, pedestrian detection from in-vehicle camera images is becoming an crucial technology for Intelligent Transportation Systems (ITS). However, it is difficult to detect pedestrians accurately in various scenes by obtaining training samples. To tackle this problem, we propose a method to construct scene dependent classifiers to improve the accuracy of pedestrian detection. The proposed method selects an appropriate classifier based on the scene information that is a category of appearance associated with location information. To construct scene dependent classifiers, the proposed method introduces generative learning for synthesizing scene dependent training samples. Experimental results showed that the detection accuracy of the proposed method outperformed the comparative method, and we confirmed that scene dependent classifiers improved the accuracy of pedestrian detection.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

QRコードは二次元コードの一種で，小面積に印刷するだけで様々な媒体に情報を付加することができ，製造における発注システムなどで利用されている．しかし，カメラとQRコードが離れるほどQRコード画像は相対的に低解像度となるため，認識が困難になる．そこで本論文では，低解像度のQRコード画像の認識性能の向上を目的として，QRコードの特徴を利用した複数フレーム超解像手法を提案する．本手法では，QRコードの認識が検出とデコードの2段階の処理からなることを利用して，それぞれの精度向上を行うために2段階の超解像処理を導入する．また，QRコードが2値のグリッド状のパターンで構成されることを利用し，これを超解像処理の拘束として用いる．更に，全てのQRコードが共通してもつパターンを利用して，ぼけを表す点拡がり関数(PSF：Point Spread Function)の推定を行う．実動画像を利用した認識実験の結果，撮影動画像そのままでは認識できないほど低解像度のQRコード画像に対して，提案手法は高い認識率を示した．このことから，提案手法の有効性を確認した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We propose a method for detecting biased-highlights in a broadcast sports video according to viewers’ attributes obtained from a large number of tweets. Recently, Twitter is widely used to make real-time play-by-play comments on TV programs, especially on sports games. This trend enables us to effectively acquire the viewers’ interests in a large mass. In order to make use of such tweets for highlight detection in broadcast sports video, the proposed method first performs an attribute analysis on the set of tweets issued by each user to classify which team he/she supports. It then detects biased-highlights by referring to the number of tweets made by viewers with a specific attribute.

DOI： 10.1007/978-3-642-35728-2_35

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Gaze estimation from an image is an important technique for tasks such as driver monitoring and measuring of advertising effectiveness. For this, most existing methods require a high quality image of eyes. However, It is difficult to obtain the eye images when eye occlusion occurs due to sunglasses or face rotation. Another approach approximates head directions to gaze directions. However, the gaze direction is affected not only by the head direction but also by the body direction. Thus, we are studying a method that estimates the gaze direction accurately using information on both head and body pose directions. Experimental result showed that the proposed method could estimate gaze directions more accurately than by using the information on only head directions.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a method for estimating the human performance of pedestrian detectability from in-vehicle camera images in order to warn a driver of the positions of pedestrians in an appropriate timing. By introducing features related to visual search and motion of the target, the proposed method estimates the detectability of pedestrians accurately. Support Vector Regression (SVR) is used to estimate the detectability. Here, SVR is trained using features calculated by the proposed method with the ground truth obtained through experiments with human subjects. From experiments using in-vehicle camera images, we confirmed that the proposed features were effective to estimate the detectability of pedestrians.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper presents an approach for realistic virtual view generation using appearance clustering based local view transition model, with its target application on cross-pose face recognition. Previously, the traditional global pattern based view transition model (VTM) method was extended to its local version called LVTM, which learns the linear transformation of pixel values between frontal and non-frontal image pairs using partial image in a small region for each location, rather than transforming the entire image pattern. In this paper, we show that the accuracy of the appearance transition model and the recognition rate can be further improved by better exploiting the inherent linear relationship between frontal-nonfrontal face image patch pairs. For each specific location, instead of learning a common transformation as in the LVTM, the corresponding local patches are first clustered based on appearance similarity distance metric and then the transition models are learned separately for each cluster. In the testing stage, each local patch for the input non-frontal probe image is transformed using the learned local view transition model corresponding to the most visually similar cluster. The experimental results on a real-world face dataset demonstrated the superiority of the proposed method in terms of recognition rate.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，自車前方を撮影した車載カメラ映像を用いた高精度な自車位置推定手法を提案する．提案手法では，車載カメラ映像と位置情報付き市街地映像データベースのフレーム対応付けを行い，対応づけられたデータベース中のフレームがもつ位置情報を参照することで自車位置推定を行う．同じ道路で撮られた映像同士であっても，走行速度や軌跡は異なるため，単純な画像照合による対応付けは困難である．そこで，画像特徴による照合ではなく，カメラ間の位置関係を反映したフレーム間の距離尺度を用いることで，フレーム対応付け精度の改善を図る．まず車載カメラ映像とデータベースのフレーム間で対応点を求め，それらからフレーム間の基礎行列を求める．次に，基礎行列から算出されるエピポールの位置を用いてカメラ間の距離を算出する．そして，これを距離尺度とするDPマッチングにより，フレーム同士を対応づける．実際の車載カメラ映像を用いた評価実験の結果，提案手法の有効性を確認した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a method for detecting general obstacles on a road by subtracting present and past in-vehicle camera images.
The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning-based methods detect only specific obstacles.
To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-vehicle camera image sequences, and then registrates road surfaces between the frames.
Finally, obstacles are detected by applying image subtraction to the registrated road surface regions with an illumination insensitive feature for robust detection.
Experiments were conducted by using several image sequences captured by an actual in-vehicle camera to confirm the effectiveness of the proposed method.
The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even in situations with different illuminations.

DOI： 10.1007/978-3-642-33868-7_51

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

The aim of this work is to support a driver by
notifying the information of traffic signals in accordance with
their visibility. To avoid traffic accidents, the driver should
detect and recognize surrounding objects, especially traffic
signals. However, when driving a vehicle under rainy weather
conditions, it is difficult for drivers to detect or to recognize
objects existing in the road environment in comparison with fine
weather conditions. Therefore, this paper proposes a method
for estimating the visibility of traffic signals for drivers under
rainy weather conditions by image processing. The proposed
method is based on the concept of visual noise known in the
field of cognitive science, and extracts two types of visual noise
features which ware considered that they affect the visibility of
traffic signals. We expect to improve the accuracy of visibility
estimation by combining the visual noise features with the
texture feature introduced in a previous work. Experimental
results showed that the proposed method could estimate the
visibility of traffic signals more accurately under rainy weather
conditions.

DOI： 10.1109/ITSC.2012.6338838

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/EST.2012.7

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ICME.2012.162

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1142/S1793351X12400077

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s11548-011-0626-9

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-3-642-29934-6_15

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.media.2010.11.001

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Recently, pedestrian detection from in-vehicle camera images is becoming an important technology in ITS (Intelligent Transportation System).
However, it is difficult to detect pedestrians stably due to the variety of their poses and their backgrounds.
To tackle this problem, we propose a method to detect various pedestrians from in-vehicle camera images by using multiple classifiers corresponding to various pedestrian pose classes.
Since pedestrians’ pose varies widely, it is difficult to construct a single classifier that can detect pedestrians with various poses stably.
Therefore, this paper constructs multiple classifiers optimized for variously posed pedestrians by classifying pedestrian images into multiple pose classes.
Also, to reduce the bias and the cost for preparing numerous pedestrian images for each pose class for learning, the proposed method employs a generative learning method.
Finally, the proposed method constructs multiple classifiers by using the synthesized pedestrian images.
Experimental results showed that the detection accuracy of the proposed method outperformed comparative methods, and we confirmed that the proposed method could detect variously posed pedestrians stably.

DOI： 10.5220/0003817305670572

担当区分：筆頭著者   記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，車載カメラ映像から多様な変化を含む標識画像を自動収集することにより，高精度な標識検出器を低コストで構築する手法を提案する．さまざまな環境で精度良く標識を検出するためには，多様な変化を含む標識画像を大量に収集し，それらを学習サンプルとして標識検出器を構築する必要がある．しかしながら，そのような標識の学習サンプルをさまざまな環境で網羅的に収集することは不可能である．また，遠く離れた標識の画像中のサイズは小さく，それらを自動もしくは半自動で大量に収集することは困難である．一方，ある時点では遠くの標識であったとしても，自車の走行に伴って遠くの標識も徐々に近づき，最終的には大きく撮影されるという特徴がある．このような大きく撮影された標識は自動検出可能であり，また，標識の時間方向への追跡も比較的容易である．そこで本論文では，まず近くで大きく撮影された標識を検出し，一時的に記憶しておいた過去の映像から遡及的に時間を遡りながら標識を追跡することによって，遠く離れた位置の低品質の標識を切り出して収集し，学習サンプルとして標識検出器を構築する手法を提案する．これにより，多様な変化を含む標識画像を自動で収集し，高精度な標識検出器をわずかな手作業で構築可能な手法を実現する．提案手法をさまざまな環境で撮影した車載カメラ映像に適用した結果，わずかな手作業でF値が約0.957の標識検出器が構築可能であることを確認した．

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

近年，高機能カーナビゲーションなどへの応用を目的とし，空撮画像と車載カメラ画像に共通して撮影されている道路面を対応づけることで高精度に自車位置を推定する手法が提案されている．従来は，道路面に印字されている路面標示の重心を特徴点として対応付けに用いていたが，このような特徴点は路面標示の欠損や遮へいに影響を受けやすいため，自車位置推定精度が十分ではなかった．そこで，本論文では，特徴点が十分に得られない場合においても，高精度，かつ頑健な自車位置推定手法を提案する．提案手法のアプローチは，（1）路面標示のコーナを特徴点とした空撮画像と車載カメラ画像の対応付け，（2）複数時刻の車載カメラ画像から得られる時系列の特徴点の対応付け，の二つである．実験の結果，前方車両が存在することにより路面標示が遮へいされるような実際の走行環境下において，提案手法による自車位置推定精度が従来手法に対して改善したことを確認した．

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，背景とのコントラストおよび道路標識の見えに基づく画像特徴の統合利用による道路標識の視認性推定手法を提案する．
近年，様々な運転支援システムが自動車に搭載されるようになってきているが，過度の情報提供はドライバの注意散漫の原因となる．
そこで本研究では，道路交通において重要な情報源となる道路標識を対象とし，道路標識の視認性に応じてドライバへ情報を提供する不快感のない運転支援システムの実現を目指す．
提案手法では，道路標識の視認性に大きく影響する要因として，背景との明るさ，色，複雑さのコントラストに基づく画像特徴，および，道路標識自体の明るさ，色に基づく画像特徴を車載カメラ画像から抽出する．
そして，認知科学的な知見に基づいてこれらの画像特徴を統合し，道路標識の視認性を定量化する．
評価実験の結果，提案手法により高精度な視認性推定ができることを確認した．
また，提案手法で利用する複数の画像特徴およびそれらの統合モデルの有効性を確認した．

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

我々は，日々増え続ける大量の放送映像からなるアーカイブを効果的かつ効率的に利用するために，映像内容に基づく構造化に取り組んでいる．本稿では，共通の枠組みの中で，様々な種類の放送映像を対象とした構造化を実現するための手段として，準同一（near-duplicate）映像の出現パターンに基づく分類を検討した結果を報告する．実際に，1,000 時間規模の放送映像に対する実験により，約360 万対の準同一映像区間を検出し，それを約4 万個のクラスタにまとめた後，6 種類の準同一映像クラスに分類した結果について分析することで，提案手法の効果と課題を明らかにする．

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

近年Web上には大量のジオタグ（撮影時のGPS情報）付き写真がアップロードされており，旅行を計画中のユーザは，旅行先の雰囲気を事前に視覚的に知ることができる．しかしながら，既存の写真共有サービスなどは単純に写真を地図上に配置するだけであるため，旅行先に関する土地勘がないユーザが必要とする情報を直感的に得ることは難しい．そこで，我々は大量の写真を風景によって分類し，多くの人が注
目した風景の写真を，旅行を計画するユーザに対して直感的に分かりやすく表現した地図である「観光マップ」を提案する．本稿では，観光マップ作成手法およびその有効性に関する初期的な検討結果について報告する．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We propose a method to detect the inconsistency between a subject and the speaker for extracting speech scenes from news videos.
Speech scenes in news videos contain a wealth of multimedia information, and are valuable as archived material.
In order to extract speech scenes from news videos, there is an approach that uses the position and size of a face region.
However, it is difficult to extract them with only such approach, since news videos contain non-speech scenes where the speaker is not the subject, such as narrated scenes.
To solve this problem, we propose a method to discriminate between speech scenes and narrated scenes based on the co-occurrence between a subject's lip motion and the speaker's voice.
The proposed method uses lip shape and degree of lip opening as visual features representing a subject's lip motion, and uses voice volume and phoneme as audio feature representing a speaker's voice.
Then, the proposed method discriminates between speech scenes and narrated scenes based on the correlations of these features.
We report the results of experiments on videos captured in a laboratory condition and also on actual broadcast news videos.
Their results showed the effectiveness of our method and the feasibility of our research goal.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，調理映像中の「切る」や「混ぜる」といった繰返し動作を検出する手法について検討した結果を報告する．繰返し動作の検出には，映像フレーム中の動作位置に依存しない特徴（CHLAC特徴）と依存する特徴（2値特徴）を用いる．これにより，繰返し動作領域の位置が大きく移動する動作及び一定である動作の両方に対応する．実際の料理番組映像に本手法を適用した結果，F値0.86の精度で検出できることを確認した．

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

近年，市街地映像を利用した運転支援システムが注目されている．また，“Googleストリートビュー”のような，市街地映像をインターネット上で閲覧できるサービスも提供されている．しかし，画像中に車両や歩行者などの移動物体が映り込むことが，遮へいやプライバシー保護の点で問題となっている．そこで本論文では，複数の車載全方位カメラ映像の部分画像を選択して貼り合わせることにより，移動物体を含まない映像を生成する手法を提案する．まず，カメラを搭載した車両が同じ経路を複数回走行することで，異なる時刻の複数の映像を収集する．そして，これらの映像間の時間方向と空間方向のレジストレーションを行う．次に，画像の同位置において移動物体が映っている頻度は少ないという仮定のもと，定義した評価関数を最小化することで，背景らしい部分画像を選択する．最後に，選択された部分画像を張り合わせることにより，移動物体を含まない市街地映像を作成する．市街地を撮影した実際の全方位カメラ映像を用いた実験により，移動物体の領域の96.5 %を除去した映像を生成することに成功した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

In order to estimate accurate rotations of mobile robots and vehicle, we propose a hybrid system which combines a low-cost monocular camera with gyro sensors. Gyro sensors have drift errors that accumulate over time. On the other hand, a camera cannot obtain the rotation continuously in the case where feature points cannot be extracted from images, although the accuracy is better than gyro sensors. To solve these problems we propose a method for combining these sensors based on Extended Kalman Filter. The errors of the gyro sensors are corrected by referring to the rotations obtained from the camera. In addition, by using the reliability judgment of camera rotations and devising the state value of the Extended Kalman Filter, even when the rotation is not continuously observable from the camera, the proposed method shows a good performance. Experimental results showed the effectiveness of the proposed method.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes a method for low resolution QR-code recognition. A QR-code is a two-dimensional binary symbol that can embed various information such as characters and numbers. To recognize a QR-code correctly and stably,the resolution of an input image should be high. In practice,however, recognition of a QR-code is usually difficult due to
low resolution when it is captured from a distance. In this paper, we propose a method to improve the performance of low resolution QR-code recognition by using the super-resolution technique that generates a high resolution image from multiple low-resolution images. Although a QR-code is a binary pattern, it is observed as a grayscale image due to the degradation through the capturing process. Especially the pixels around the borders between white and black regions become ambiguous. To overcome this problem, the proposed method introduces a binary pattern constraint to generate super-resolved images appropriate for recognition. Experimental results showed that a recognition rate of 98% an be achieved by the proposed method, which is a 15.7% improvement in comparison with a method using a conventional super-resolution method.

記述言語：英語   掲載種別：研究論文（学術雑誌）  

A power-efficient K-Means hardware architecture that can automatically estimate the number of clusters in the clustering process is proposed. The contributions of this work include two main aspects. The first is the integration of the hierarchical data sampling in the hardware to accelerate the clustering speed. The second is the development of the Bayesian-Information-Criterion (BIC) Processor" to estimate the number of clusters of K-Means. The architecture of the "BIC Processor" is designed based on the simplification of the BIC computations, and the precision of the logarithm function is also analyzed. The experiments show that the proposed architecture can be employed in different multimedia applications, such as motion segmentation and edge-adaptive noise reduction. Besides, the gate count of the hardware is 51 K with the 90-nm complimentary metal-oxide-semiconductor technology. It is also shown that this work can achieve high efficiency compared with a GPU, and the power consumption scales well with the number of clusters and the number of dimensions. The power consumption ranges between 10.72 and 12.95 mW in different modes when the operating frequency is 233 MHz."

DOI： 10.1109/JETCAS.2011.2165231

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

既存のテキスト料理レシピから初心者に分かりにくい表現を検出した後，大量の料理レシピから抽出した情報に基づいてテキストを，調理動作の種類に応じて画像・映像を補足することで，分かりやすいマルチメディア料理レシピを作る方法を検討した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

We propose a visibility estimation method for traffic signs considering temporal environmental changes, as a part of work for the realization of nuisance-free driver assistance systems. Recently, the number of driver assistance systems in a vehicle is increasing. Accordingly, it is becoming important to sort out appropriate information provided from them, because providing too much information may cause driver distraction. To solve such a problem, we focus on a visibility estimation method for controlling the information according to the visibility of a traffic sign. The proposed method sequentially captures a traffic sign by an in-vehicle camera, and estimates its accumulative visibility by integrating a series of instantaneous visibility. By this way, even if the environmental conditions may change temporally and complicatedly, we can still accurately estimate the visibility that the driver perceives in an actual traffic scene. We also investigate the performance of the proposed method and show its effectiveness.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Road image is becoming important for several
applications such as car navigation systems, traffic environment
research, city modeling. Usually, a road image can be obtained
from an aerial image but the resolution of the aerial image is
often low, or it contains occlusions by obstacles. Therefore, the
update of road image is required. In this paper, we propose a
road image mosaicing method using in-vehicle camera images
and an aerial image. We first perform image registration of road
regions between these images, and then, we generate a large road
image by performing image mosaicing of road regions in invehicle
camera images. In an experiment, we achieved resolution
improvement and occlusions removal, and also succeeded in
update of a large road image.

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Free space detection is very important for vehicle navigation and safe driving. 3-D line segment reconstruction
of a street is important for the free space detection because a street-view includes many line segments. For the free space
detection, we propose a method for reconstructing 3-D line segments in a streetscape using a monocular in-vehicle camera.
The 3-D reconstruction of the line segments is achieved by using each three images from an image sequence. Once accurate
camera poses of these images are obtained, one of the remaining crucial problems is to match the line segments between the
images correctly. A strategy for finding correspondence of the line segments is as follows: First, the correspondences of line
segment candidates are searched by using a two-view constraint. However, the two-view constraint has difficulty on determining
an unique correspondence geometrically. Therefore, the candidates of the line segment correspondences are reduced using
a three-view constraint. In order to improve the accuracy, the proposed method exploits a color feature of the line segment
and a preliminary knowledge of the vehicle motion. Finally, the line segments are reconstructed using the correspondences.
From an experimental result, we confirmed the effectiveness of the proposed method. Application to the free space detection
demonstrated the usefulness of the reconstructed line segments.

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper proposes an intelligent traffic sign detector using adaptive learning based on online gathering of training samples from in-vehicle camera image sequences. To detect traffic signs accurately from in-vehicle camera images, various training samples of traffic signs are needed. In addition, to reduce false alarms, various background images should also be prepared before constructing the detector. However, since their appearances vary widely, it is difficult to obtain them exhaustively by manual intervention. Therefore, the proposed method simultaneously obtains both traffic sign images and background images from in-vehicle camera images. Especially, to reduce false alarms, the proposed method gathers background images that were easily mis-detected by a previously constructed traffic sign detector, and re-trains the detector by using them as negative samples. By using retrospectively tracked traffic sign images and background images as positive and negative training samples, respectively, the proposed method constructs a highly accurate traffic sign detector automatically. Experimental results showed the effectiveness of the proposed method.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

In this study, we propose a method for detecting road markings recorded in an image captured by an in-vehicle camera by using a position-dependent classifier. Road markings are symbols painted on the road surface that help in preventing traffic accidents and in ensuring traffic smooth. Therefore, driver support systems for detecting road markings, such as a system that provides warning in the case when traffic signs are overlooked, and supporting the stopping of a vehicle are required. It is difficult to detect road markings because their appearance changes with the actual traffic conditions, e. g. the shape and resolution change. The variation in these appearances depend on the positional relation between the vehicle and the road markings, and on the vehicle posture. Although these variations are quite large in an entire image, they are relatively small in a local area of the image. Therefore, we try to improve the detection performance by taking into account the local variations in these appearances. We propose a method in which a position-dependent classifier is used to detect road markings recorded in images captured by an in-vehicle camera. Further, to train the classifier efficiently, we propose a generative learning method that takes into consideration the positional relation between the vehicle and road markings, and also the vehicle posture. Experimental results showed that the detection performance when the proposed method was used was better than when a method involving a single classifier was used.

DOI： 10.1541/ieejias.131.466

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

In order to estimate accurate attitude of mobile robots and　vehicles, we propose a hybrid system which combines a lowcost　monocular camera with a gyro sensor. A gyro sensor has　drift errors that accumulate over time. On the other hand, a　camera cannot obtain the rotation continuously in the case　where feature points cannot be extracted from images, although　the accuracy is better than a gyro sensor. To solve　these problems, we propose a method for combining these　sensors based on an Extended Kalman Filter.

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，データベース作成時に全方位カメラ，位置推定時に通常カメラを使用するアピアランスベースの自車位置推定手法を提案する．従来のアピアランスベースの自車位置推定では，データベース作成時と位置推定時で同一種類のカメラが使用されることが多い．しかし，位置推定精度やカメラの設置位置の制約などの点で問題となることがある．提案手法では，通常カメラ映像のフレームに対応する全方位カメラ映像のフレームと，そのフレーム中の領域を探索する．しかし，市街地では類似する物体が多く存在し，単一フレーム照合では正確な照合が難しい．そのため，複数フレーム照合を用いることで精度の向上を図る必要がある．これを実現するため，フレームと領域を考慮した映像間の照合コストを定式化する．そして，動的計画法によりこれを最小化するためのアルゴリズムを導入し，このアルゴリズムを用いて映像全体で最適に対応づける．データベース内のフレームには位置情報が付加されており，これにより自車位置を得る．実際に市街地で撮影した映像を用いた評価実験の結果，自車位置推定誤差が2m以下の割合が，領域の対応付けを行わない手法で77%，提案手
法で90%であり，提案手法の有効性を確認した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

環境変化に対してロバストかつ高速な物体検出を可能とする識別器として，Violaらが提案したカスケード型識別器がある．これを用いて様々な環境下に存在する道路標識を高精度に検出するためには，多様な見えの変動を含んだ大量の標識画像を用いて識別器の学習を行う必要がある．しかしながら，多様な見えの変動をバランスよく含んだ大量の標識画像を手作業で収集するには多大なコストがかかる．そのため，本研究では生成型学習を用いてカスケード型識別器を構築することを考える．これまでにも生成型学習を用いて識別器を構築
する手法は提案されているが，形状やテクスチャの変動のみが考慮されており，色の変動は考慮されていない．
そこで本論文では，形状やテクスチャの変動に加えて色の変動も考慮し，より高い検出性能をもったカスケード型識別器を構築する手法を提案する．車載カメラ映像を用いた道路標識の検出実験の結果，本論文で提案する色の変動を考慮する生成型学習により，従来よりも高い検出性能をもつカスケード型識別器が構築できることを確認した．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ICPR.2010.844

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ICPR.2010.766

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/IVS.2010.5548137

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ISM.2009.67

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

This paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3D CT images based on machine learning and combination optimization. We also show applications of anatomical labeling on a bronchoscopy guidance system. This paper performs automated labeling by using machine learning and combination optimization. The actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from CT images, (b) construction of AdaBoost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. We applied the proposed method to 90 cases of 3D CT datasets. The experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. Also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.

DOI： 10.1007/978-3-642-04271-3_86

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/ICDAR.2009.168

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.media.2009.06.001

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/IVS.2009.5164275

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/IVS.2009.5164417

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本稿では，臓器・疾病横断型の診断支援を実現するための共通基盤システムについて述べる．医用画像に含まれる多量の情報を最大限に活用することが可能な臓器・疾病横断型の診断支援は，医師の負担軽減，診断の質的向上を実現する上で，今後，必要不可欠なると考えられる．我々は，この次世代の診断支援を実現するために共通基盤システムを開発している．この共通基盤システムは，診断支援機能を統合化するための仕組みを備えており，臓器・疾病横断型の診断支援や医師の診断ワークフローに沿う診断支援を実現することができる．本稿では，この共通基盤システムおよびその構成技術について詳述し，既存の医用画像処理ソフトウェアと比較実験を行い，本共通基盤システムが，多臓器・多疾病を対象とする計算機支援医用画像診断システムを実現するための優れた拡張性を有していることを

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1111/j.1442-2042.2006.01561.x

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では，造影3次元腹部X線CT像からCT値の確率分布を解析することにより，肝臓領域を自動抽出する手法について述べる．肝臓のCT値分布は隣接する脾臓や筋肉のCT値分布と非常に類似しているため，単一時相からしきい値処理を用いて肝臓領域を抽出することは困難である．本論文では，早期相と晩期相の2次元ヒストグラムから肝臓，脾臓，筋肉に対応するCT値分布を推定し，肝臓領域抽出に用いるしきい値を自動的に決定する．具体的には，各臓器のCT値分布を正規分布と仮定し，EMアルゴリズムを用いてそれぞれの分布を推定する．推定された分布を用いて，肝臓領域，肝細胞がん領域を抽出することで，肝細胞がんを含む肝臓領域を抽出する．また，肝臓外領域を抽出することで肝臓に隣接する筋肉等への過抽出を抑制し，最後に輪郭を補正し肝臓領域を得る．本手法を早期相，晩期相の3次元腹部X線CT像26例に適用した結果，24例で良好に肝臓領域を抽出することが可能であった．

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

This paper discusses the virtual prostate needle biopsy system and the construction of the virtual prostate model, considering the actual distribution of prostate abnormalities. A needle biopsy simulation is performed for the constructed virtual prostate, and the actual biopsy procedure in the clinical situation is evaluated. The prostate needle biopsy is a histologic diagnosis procedure in which a sample is acquired from the prostate tissue by needle biopsy and is inspected under a microscope. In order to achieve reliable prostate needle biopsy, it is necessary to consider systematically and numerically the number of needles required, and their locations and insertion angles.
For such a purpose, a system is developed in which a virtual prostate is constructed on a computer and the biopsy procedure is evaluated quantitatively by performing virtually the prostate needle biopsy. Furthermore, two different models are constructed, for the prostate with and without hypertrophy, based on the actual statistical distribution data for
prostate abnormalities. Each model is partitioned into the peripheral zone and the transition zone. The constructed
virtual prostate is input into the virtual needle biopsy simulation system, and three different systematic biopsy procedures actually used at clinical sites, and four different needle biopsy procedures, are experimentally evaluated.
The experiments show that the insertion angle that maximizes the hit probability is not always the same as the
insertion angle that maximizes cancer sample acquisition. It is evident that the proposed method can indicate a biopsy
procedure which realizes a high hit probability with a small number of needles.

DOI： 10.1002/scj.20181

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1016/j.ics.2005.03.282

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本稿では，造影3次元腹部X線CT像から肝臓領域を自動抽出する手法について述べる．肝臓の診断では複数の時相のCT画像を用いるため，肝臓を対象としたコンピュータ支援診断システムにおいては，複数の時相から肝臓領域を抽出することは非常に重要である．本稿では特に肝細胞がんの診断に重要とされる早期相，晩期相からの肝臓領域抽出を行う．まず，晩期相においてCT値ヒストグラムを解析し，肝臓に対応するCT値の範囲を自動決定し，しきい値処理によりおおまかな肝臓領域を抽出する．次に，ユークリッド距離に基づく図形分割・統合処理により肝臓に接している他臓器を除去し，最後に輪郭を補正し肝臓領域を得る．早期相に対しては，晩期相から抽出した肝臓領域を早期相のCT像にマッピングし，輪郭領域を修正することで肝臓領域を得る．本手法を早期相，晩期相の3次元腹部X線CT像19症例に適用した結果，ほぼ良好に肝臓領域を抽出することが可能であった．

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

担当区分：筆頭著者   記述言語：日本語   掲載種別：研究論文（学術雑誌）  

本論文では,前立腺針生検シミュレーションシステムと実際の前立腺病変分布を考慮した仮想前立腺モデルの作成について述べる.また,作成した仮想前立腺に対する針生検シミュレーションを行い,実際の臨床で用いられている生検手法の評価を行う.前立腺針生検とは,前立腺内の組織を生検針により採取し,顕微鏡で検査する組織診断方法である.確実な前立腺針生検を行うには,針の本数,配置法,挿入角度等を組織的かつ数値的に検討する必要がある.そこで,計算機上に仮想的な前立腺を作成し,仮想的な前立腺針生検を行うことで生検手法を定量的に評価するシステムを開発した.また,実際の前立腺における病変分布の統計的なデータをもとに,肥大症の前立腺とそうでない前立腺の2種類のモデルを作成し,それぞれのモデルに対し辺縁領域(peripheral zone),移行領域(transition zone)への分類を行った.作成した仮想前立腺を仮想針生検シミュレーションシステムに入力し,臨床で用いられている系統的分割生検3種類と別の4種類の生検手法に対する評価実験を行った.実験の結果,ヒット確率を最大にする穿刺角度とがんの採取量を最大にする穿刺角度は必ずしも一致せず,少ない針の本数で高いヒット確率を得る生検手法の存在を,本システムを用いて採すことが可能であることを確認した.

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1007/978-3-540-39899-8_50

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/S1361-8415(02)00089-0

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語  

警察の捜査では，大量の監視カメラ映像の中から対象の人物を探すことが，膨大な人的コストをかけて人手で行われている．これを画像処理によって監視カメラ映像を絞り混むことで支援する技術が求められている．そこで我々は監視カメラ映像中から検出された人物画像から手荷物所持の有無を判定することにより，人物絞り込みの支援を行うことを目的として研究している．人物絞り込みは，漏れをいかに減らすかが重要であるが，1枚の画像から手荷物所持の有無を判定する場合，体により手荷物が隠れてしまい正しく判定できない場合がある．そこで，本稿では我々が提案した，複数方向から撮影された人物画像を利用した手荷物所持判定手法について解説する．

記述言語：日本語  

カメラを用いた群集解析は公共の安全やマーケテイングなどに需要がある。我々は群集解析の中でも、小領域毎の人数を求める空間的な人数分布推定の実現を目指している。 しかし、カメラから離れた場所では人物閣の遮蔽が発生するため、空間的な人数分布を正しく推定することは困難である。そこで本稿では、複数視点、のカメラから撮影された画像を用いて記憶型回帰により人数分布を推定する手法を紹介する。本手法は、人数分布と群集の見えの対応表を用いるものであり、予め群集の見えに遮蔽が含まれているため、遮蔽に頑健な人数分布推定手法が実現される。

記述言語：日本語  

本稿では、空撮画像と車載カメラ画像の対応付けにより自車の位置を推定する手法について述べた．提案手法は、複数時刻の車載カメラ画像を利用することで、自車位置の推定性能の向上を図ったものである．実験により、推定性能の向上を確認し、提案手法の有効性を確認する．

記述言語：日本語  

我々が普段使用しているPCには，モニターへの映像出力や3D描画処理において重要な役割を担うGPU（Graph-ics Processing Unit）が必ずといってよいほど搭載されている．また，PlayStation 3やXbox 360等のゲーム機にも高性能なGPUが搭載されるようになっており，その性能を活かした臨場感あふれるゲームを体験できるようになった．特に，ここ数年のGPUの性能向上は著しく，その演算性能が1TFLOPS（FLoating point Operations Per Second）を超える製品も発売されるようになっている．従来，GPUは2Dや3Dの描画処理を担当する演算装置であったが，近年の高性能化に伴い，GPUを描画処理以外に利用しようという試みが注目を集めている．これはGPGPU（General-Purposecomputation on GPU）と呼ばれており，物理シミュレーション，数値計算，信号解析，画像処理・認識，などの様々な分野で広く利用されるようになってきている．

記述言語：日本語  

記述言語：日本語  

記述言語：日本語  

記述言語：日本語  

開催年月日： 2025年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：群馬大学  

開催年月日： 2024年

記述言語：日本語   会議種別：口頭発表（一般）  

近赤外線カメラなどでのNeRFを見据え、モノクロ画像を利用したNeRFについて研究した。モノクロ画像をカラー化してNeRFに入力する従来手法では、3次元的な色の一貫性が考慮されておらず、視点の移動により不自然に色が変化する3Dモデルが生成された。その問題を解決するために、NeRFに新たな損失を導入することで視点の移動による色の変化を抑制した。

開催年月日： 2024年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Keio University  

開催年月日： 2024年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Keio University  

開催年月日： 2024年

記述言語：英語   会議種別：ポスター発表  

開催地：Rome, Italy  

In this paper, we present a new task of detecting an object that a target pedestrian is gazing at in a traffic scene called PEdestrian's Gaze Object (PEGO). We argue that the detection of gaze object can provide important information for pedestrian's behavior prediction and can contribute to the realization of automated vehicles. For this task, we construct a dataset of in-vehicle camera images with annotations of the objects that pedestrians are gazing at. Also, we propose a Transformer-based method called PEGO Transformer to solve the PEGO detection task. The PEGO Transformer directly performs gaze object detection with the utilization of whole-body features without a high-resolution head image and a gaze heatmap which the traditional methods rely on. Experimental results showed that the proposed method could estimate pedestrian's gaze object accurately even if various objects exist in the scene.

開催年月日： 2024年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Precise House Mantegna Roma  

Deep learning technology has rapidly advanced, leading to the development of End-to-End driving models (E2EDMs) for autonomous vehicles with high prediction accuracy. To comprehend the prediction results of these E2EDMs, one of the most representative explanation methods is attribution-based. There are two kinds of attribution-based explanation methods: pixel-level and object-level. Usually, the heatmaps illustrate the importance of pixels and objects in the prediction results, serving as explanations for E2EDMs. Since there are many attribution-based explanation methods, evaluation methods are proposed to determine which one is better at improving the explainability of E2EDMs.
Fidelity measures the explanation's faithfulness to the model's prediction method, which is a bottommost property. However, no evaluation method could measure the fidelity difference between object-level and pixel-level explanations, making the current evaluation incomplete. In addition, without considering fidelity, previous evaluation methods may advertise manipulative explanations that solely seek human satisfaction (persuasibility). Therefore, we propose an evaluation method that further considers fidelity, our method enables a comprehensive evaluation that proves the object-level explanations genuinely outperform pixel-level explanations in fidelity and persuasibility, thus could better improve the explainability of the E2EDMs.

開催年月日： 2024年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：別府国際コンベンションセンター ビーコンプラザ  

注視対象物推定は，自動運転車両の実現において不可欠な歩行者の行動予測のための重要な要素である．本発表では，交通シーンで歩行者が注視している物体（PEdestrians' Gaze Object: PEGO）を推定するPEGO Transformer V2を提案する．従来手法の多くは，高解像度の頭部画像と視線ヒートマップを用いて歩行者の注視対象物を推定している．しかしながら，遠方にいる歩行者に対して，高解像度の頭部画像を得られないため推定精度が低いという問題があった．そこでPEGO Transformer V2は，歩行者全身の特徴に加え，歩行者位置情報も活用することで性能改善を図る．実験の結果，提案手法であるPEGO Transformer V2は，シーン中の歩行者ごとにそれぞれ正しい注視対象物を推定できることを確認した．

開催年月日： 2024年

記述言語：日本語   会議種別：口頭発表（一般）  

本発表では，人物の背面画像を入力として正面画像を生成する技術の実現を目的とする.この技術は，監視カメラ映像等に映る人物を人が確認する際の支援などに応用が期待できる．従来の研究では，人物の前向き画像から横向きや後ろ向きの画像を生成する際の精度に比べて，後ろ向き画像からそれ以外の方向を向いた画像を生成した時の精度が劣っていた. この問題を解決するために，本発表では拡散モデルを用いることで背面画像からより精度の高い正面画像を生成する手法を検討した.

開催年月日： 2024年

記述言語：日本語   会議種別：ポスター発表  

開催地：熊本城ホール（熊本県熊本市中央区桜町3番40号）  

開催年月日： 2024年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Shanghai, China  

開催年月日： 2024年

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2024年

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2024年

記述言語：英語   会議種別：ポスター発表  

開催地：熊本城ホール（熊本県熊本市中央区桜町3番40号）  

In autonomous driving, end-to-end driving models (E2EDMs) have become increasingly popular due to their exceptional predictive performance. This performance stems from leveraging a backbone that is pre-trained on large datasets and then fine-tuned for driving tasks. However, the opaque nature of these E2EDMs presents a challenge to explainability, thus visual explanations are created to shed light on the decision-making process of E2EDMs. Currently, these E2EDMs have been made more explainable by fine-tuning the driving tasks along with a side task designed for explainability. This side task for explainability requires complex structures with auxiliary data, such as the location of objects.
In this paper, we argue that a more effective approach to enhancing the explainability of E2EDMs is to design a fine-tuning specifically focused on explainability. We propose CROp-based COntrastive DIscriminative LEarning (CROCODILE) to enhance a backbone’s capability to accurately identify object features during this additional fine-tuning for explainability. By adopting CROCODILE, we can develop more explainable E2EDMs without relying on auxiliary data or complex structures in the fine-tuning for driving tasks. Our experimental results confirm that our approach enhances the explainability of E2EDMs.

開催年月日： 2024年

記述言語：英語   会議種別：ポスター発表  

開催年月日： 2024年

記述言語：日本語   会議種別：ポスター発表  

開催地：熊本城ホール（熊本県熊本市中央区桜町3番40号）  

開催年月日： 2024年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：InterContinental Hanoi Landmark72  

開催年月日： 2024年

記述言語：英語   会議種別：ポスター発表  

開催地：Ateneo de Manila University  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：公立はこだて未来大学  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：公立はこだて未来大学  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：公立はこだて未来大学  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：公立はこだて未来大学  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催年月日： 2023年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Maison Glad JEJU hotel, Jeju, Korea  

開催年月日： 2023年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Lisbon, Portugal  

開催年月日： 2023年

記述言語：英語   会議種別：ポスター発表  

開催地：Lisbon, Portugal  

開催年月日： 2023年

記述言語：日本語   会議種別：ポスター発表  

開催地：アクトシティ浜松  

開催年月日： 2023年

記述言語：英語   会議種別：ポスター発表  

開催地：Anchorage, AK, USA  

DOI： 10.1109/IV55152.2023.10186742

開催年月日： 2023年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：アクトシティ浜松  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：沖縄国際大学  

開催年月日： 2023年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：アクトシティ浜松  

開催年月日： 2023年

記述言語：日本語   会議種別：ポスター発表  

開催地：アクトシティ浜松  

開催年月日： 2023年

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催地：中部生産性本部 セミナールーム  

開催年月日： 2022年

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2022年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Hybrid (The Hong Kong Polytechnic University and online)  

開催年月日： 2022年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：姫路市文化コンベンションセンター アクリエひめじ  

開催年月日： 2022年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Online  

DOI： 10.1109/MIPR54900.2022.00050

開催年月日： 2022年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：姫路市文化コンベンションセンター アクリエひめじ  

開催年月日： 2022年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：姫路市文化コンベンションセンター アクリエひめじ  

開催年月日： 2022年

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2022年

記述言語：英語   会議種別：ポスター発表  

開催地：Fukuoka International Congress Center  

We analyze gaze while viewing videos at various playback speeds.
The aims of this paper are to evaluate the comprehension and viewability of videos.
In our experiment, we used 0.75x to 3.0x speed, and measured gaze.
We report differences in gaze behavior among different playback speeds in viewing same video.

開催年月日： 2022年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Universidad Autónoma de Madrid (Hybrid)  

We propose a method to detect birds in a 3D environment referring to both audio information observed from a microphone array and visual information observed from a panorama camera. In general, in panorama images, birds appear relatively too small to be detected accurately even with the state-of-the-art deep learning models. Thus, the proposed method takes a two step approach where the birds are first roughly located referring to audio information by Sound Source Localization (SSL), and then image detection is applied within its vicinity. Through evaluation on a dataset annotated with bounding boxes surrounding the birds, we show that the proposed method improves detection performance of birds that appear in relatively small sizes in the image, in both accuracy and processing speed.

DOI： 10.1109/AVSS56176.2022.9959510

開催年月日： 2022年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：熊本大学工学部黒髪キャンパス  

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2021年

記述言語：英語   会議種別：ポスター発表  

開催地：Milan, Italy (Virtual)  

In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy using an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

開催年月日： 2021年

記述言語：英語   会議種別：ポスター発表  

開催地：Milan, Italy (Virtual)  

In this paper, we propose an occlusion-robust pose
estimation method of an unknown object instance in an
object category from a depth image. In a cluttered
environment, objects are often occluded mutually. For
estimating the pose of an object in such a situation, a
method that de-occludes the unobservable area of the object
would be effective. However, there are two difficulties;
occlusion causes offset between the actual object center
and the center of its observable area, and different
instances in a category may have different shapes. To cope
with these difficulties, we propose a two-stage
Encoder-Decoder model to extract features with objects
whose centers are aligned to the image center. In the
model, we also propose the Median-shape Reconstructor as
the second stage to absorb shape variations in a category.
By evaluating the method with both a large-scale virtual
dataset and a real dataset, we confirmed the proposed
method achieves good performance on pose estimation of an
occluded object from a depth image.

開催年月日： 2021年

記述言語：英語   会議種別：ポスター発表  

開催地：Milan, Italy (Virtual)  

In this paper, we propose Object Manifold Embedding GAN (Ω-GAN) to generate images of variously shaped and arbitrarily posed objects from a noise variable sampled from a distribution defined over the pose and the shape manifolds in a vector space. We introduce Parametric Manifold Sampling to sample noise variables from a distribution over the pose manifold to conditionally generate object images in arbitrary poses by tuning the pose parameter. We also introduce Object Identity Loss for clearly disentangling the pose and shape parameters, which allows us to maintain the shape of the object instance when only the pose parameter is changed. Through evaluation, we confirmed that the proposed Ω-GAN could generate variously shaped object images in arbitrary poses by changing the pose and shape parameters independently. We also introduce an application of the proposed method for object pose estimation, through which we confirmed that the object poses in the generated images are accurate.

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

ECサイトでの商品検索には，商品名や説明文中のキーワードが用いられる．しかし，検索エンジン最適化（SEO）対策として，商品との関連性が低いキーワードが付与されている場合，検索結果の信頼性が低く，直感的な検索もできない．そこで本研究では，商品画像から受ける印象に基づく，直感的な商品検索の実現を目指している．商品画像の印象は商品を検索する顧客の属性により異なることが想定されるため，本発表では，検索者の年代や性別などの属性に応じた商品画像の印象推定法を検討した．属性を考慮しないベースライン手法と，属性を考慮する提案手法を比較した結果，属性を考慮することで，商品画像の印象をより正確に推定できることを確認した．

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

近年，不審物検出の需要が高まっている.不審物は重いものが多いため，所持物の重さ認識は不審物検出の重要な手掛かりになりうる.
人の歩き方は所持物の重さによって違いがあるため，人の歩き方の特徴に着目した所持物・重さ認識手法を検討する.
本研究では，人物骨格の歩行１周期分の系列を入力とし，所持物・重さを表すクラスを出力とする.
学習段階では，RTWのランダム選択により，歩行１周期分の系列から複数の固定長部分系列を作成し，元の系列と同じラベルを付与する.
次に各固定長部分系列を用いて，ST-GCNに基づく所持物・重さ認識器を学習する.
認識段階では，歩行１周期分の系列から複数の固定長部分空間を選択し，それぞれを学習済みの所持物・重さ認識器に入力する.出力の多数決を行い，最も数の多い所持物・重さを出力する.
所持物のみの認識と所持物と重さの認識を行った.
所持物のみの認識は高精度だが，所持物と重さの認識では依然低精度なため，さらなる改良が必要である.

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

車両の運転において, 歩行者が車両に気づいているか否かを知る上で, アイコンタクトは重要な判断材料となる. 従来の手法では, 直接的な視線推定技術を用いており, 至近距離で撮影した画像にのみ対応しており, カメラから離れた位置にいる歩行者については対応していないという問題がある. 一方, 我々は視線が取れない遠方の歩行者に対しても, 全身の情報を用いてアイコンタクトの有無を区別できる. これに倣い, 本発表では, 視線推定が困難な遠方歩行者からのアイコンタクトの有無を顔や体の情報を複合的に用いて検出する手法を検討する.
PIEデータセットを用いた実験によって, 85%の正解率で検出が可能であることを確認した.

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：Online  

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：Online  

開催年月日： 2021年

記述言語：英語   会議種別：ポスター発表  

開催地：Virtual  

For computers to understand human perception, metrics that can capture human perception well are important. However, there are few metrics that characterize the visual perception of humans towards images. Therefore, in this paper, we propose a novel concept and a metric of pointedness of an image, which describes how pointy an image is perceived. The algorithm is inspired by the Features from Accelerated Segment Test (FAST) algorithm for corner detection which looks on the number of continuous neighboring darker pixels surrounding each pixel. We assume that this number would be proportional to the perceived pointedness in the region around the pixel. We evaluated our method towards how well it could capture the human perception of images. To compare the method with similar metrics that describe shapes, we prepared silhouette images of both artificial shapes and natural objects. The results showed that the proposed method gave nearly equivalent perceptual performance to other metrics and also worked in a larger variety of images.

DOI： https://doi.org/10.1007/978-3-030-78635-9_20

開催年月日： 2021年

記述言語：日本語   会議種別：ポスター発表  

開催地：online  

開催年月日： 2021年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Online  

DOI： 10.1007/978-3-030-67835-7_6

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：Online  

開催年月日： 2021年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：Online  

開催年月日： 2020年

記述言語：英語   会議種別：ポスター発表  

開催地：Dublin, Ireland (Virtual)  

Imageability is a concept from Psycholinguistics quantizing the human perception of words. However, existing datasets are created through subjective experiments and are thus very small. Therefore, methods to automatically estimate the imageability can be helpful. For an accurate automatic imageability estimation, we extend the idea of a psychological hypothesis called Dual-Coding Theory, that discusses the connection of our perception towards visual information
and language information, and also focus on the relationship between the pronunciation of a word and its imageability. In this research, we propose a method to estimate imageability of words
using both visual and language features extracted from corresponding data. For the estimation, we use visual features extracted from low- and high-level image features, and language features extracted from textual features and phonetic features of words. Evaluations how that our proposed method can estimate imageability more accurately than comparative methods, implying the contribution of each feature to the imageability.

DOI： 10.1145/3372278.3390731

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

様々な場面や用途に応じた画像キャプショニングを目指し，文の心像性に基づく画像キャプショニング手法を提案する．まず，既存の画像キャプションデータセットを拡張して，様々な抽象度のキャプションをもつ画像キャプションデータセットを構築する．そして，そのデータセット中のキャプションに対して心像性を算出することで，心像性スコア付きキャプションデータセットを構築する．それを用いてキャプショニングモデルを学習し，指定した心像性をもつ画像キャプションを生成する枠組みを構築する．評価実験では，画像キャプションデータセットの拡張の有効性と提案手法による画像キャプショニング実現の可能性を確認した．

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

本報告では，料理レシピ検索支援を目的として，料理の種類に応じて盛り付けの典型度を評価する手法を提案する．典型的な盛り付けとは，その料理の種類において頻出する特徴を備えている盛りつけである．そこで本研究では，典型的な盛り付けは出現頻度が高いことと，同一の盛り付け同士では似た画像特徴をもつことを考慮して定量的に典型度を推定する．提案手法ではまず，料理写真の背景領域を除去し，色特徴とテクスチャ特徴を抽出する．次に，特徴量を正規化し，カーネル密度推定によって密度分布を生成する．そして，ある料理写真が与えられたとき，その料理写真の特徴量と密度分布から典型度を計算する．評価実験ではカーネル密度推定を用いた典型度評価手法により，色特徴の有効性を確認した．

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：広島大学  

開催年月日： 2020年

記述言語：日本語  

開催年月日： 2020年

記述言語：日本語  

開催年月日： 2020年

記述言語：英語   会議種別：ポスター発表  

開催地：Grand Hotel Excelsior Malta  

In recent years, various facilities have been deployed to support visually impaired people. However, accidents caused by visual disabilities still occur. In this paper, to support the visually impaired people in public areas, we aim to identify whether a pedestrian image sequence obtained by a surveillance camera indicates the presence of a white cane user by analyzing the temporal transition of a human skeleton represented as 2D coordinates. Our previously proposed method aligns the orientation of the human skeletons to various orientations and identifies a white cane user from the corresponding sequences, relying on multiple classifiers related to each orientation. The method employs an exemplar-based approach to perform the alignment. However, it heavily depends on the number of exemplars and consumes excessive memory. In this paper, we propose a method to align 2D human skeleton representation sequence to various orientations using the proposed Skeleton Orientation Alignment Networks (SOANets) based on an encoder-decoder model. Using SOANets, we can obtain 2D skeleton representation sequences aligned to various orientations, extract richer skeleton features, and recognize white cane users accurately. We conducted an experiment to confirm that the
proposed method improves the recognition rate by 16%, compared to the method that does not use the skeleton
orientation alignment.

開催年月日： 2020年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：指宿ふれあいプラザなのはな館  

Surveillance cameras are expected to play a large role in the development of ITS technologies. They can be used to detect abnormally behaving individuals which can then be reported to drivers nearby. There are multiple works that tackle the problem of abnormal behavior detection. However, most of these works make use of appearance features which have redundant information and are susceptible to noise. While there are also works that make use of pose skeleton representation, they do not consider well how to handle cases with occlusions, which can occur due to the simple reason of pedestrian orientation preventing some joints from appearing in the frame clearly. In this paper, we propose a skeleton trajectory representation that enables handling of occlusions. We also propose a framework for pedestrian abnormal behavior detection that uses the proposed representation and detect relatively hard-to-notice anomalies such as drunk walking. The experiments we conducted show that our method outperforms other representation methods.

開催年月日： 2020年

記述言語：英語  

開催地：Deajeon Convention Center, Daejeon, Korea  

Recent multimedia applications commonly use text and imagery from Social Media for tasks related to sentiment research. As such, there are various image datasets for sentiment research for popular classification tasks. However, there has been little research regarding the relationship between the sentiment of images and its annotations from a multi-modal standpoint. In this demonstration, we built a tool to visualize psycholinguistic groundings for a sentiment dataset. For each image, individual psycholinguistic ratings are computed from the image's metadata. A sentiment-psycholinguistic spatial embedding is computed to show a clustering of images across different classes close to human perception. Our interactive browsing tool can visualize the data in various ways, highlighting different psycholinguistic groundings with heatmaps.

DOI： 10.3758/s13428-018-1099-3

開催年月日： 2020年

記述言語：英語   会議種別：ポスター発表  

開催地：Deajeon Convention Center, Daejeon, Korea  

A mimetic word is used to verbally express the manner of a phenomenon intuitively. The Japanese language is known to have a greater number of mimetic words in its vocabulary than most other languages. Especially, since human gaits are one of the most commonly represented behavior by mimetic words in the language, we consider that it should be suitable for labels of fine-grained gait recognition. In addition, Japanese mimetic words have a more decomposable structure than these in other languages such as English. So it is said that they have sound-symbolism and their phonemes are strongly related to the impressions of various phenomena. Thanks to this, native Japanese speakers can express their impressions on them briefly and intuitively using various mimetic words. Our previous work proposed a framework to convert the body-parts movements to an arbitrary mimetic word by a regression model. The framework introduced a phonetic space" based on sound-symbolism, and it enabled fine-grained gait description using the generated mimetic words consisting of an arbitrary combination of phonemes. However, this method did not consider the "naturalness" of the description. Thus, in this paper, we propose an improved mimetic word generation module considering its naturalness, and update the description framework. Here, we define the co-occurrence frequency of phonemes composing a mimetic word as the naturalness. To investigate the co-occurrence frequency, we collected many mimetic words through a subjective experiment. As a result of evaluation experiments, we confirmed that the proposed module could describe gaits with more natural mimetic words while maintaining the description accuracy."

DOI： 10.1007/978-3-030-37734-2_18

開催年月日： 2020年

記述言語：日本語   会議種別：ポスター発表  

開催地：オンライン  

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

本報告では，小説などの書籍を対象とし，ページ単位でその文書内容に応じた彩色を行なう手法を検討する．このような彩色を施すことにより，書籍をわざわざ開かずとも，読者がその内容の変遷を俯瞰的に知ることができるようになると期待される．提案手法では，文書中に出現する各単語に対して「人間が単語から連想する色」に関する知見を用いて色を割り当てる．そして，各ページにおいて出現確率が高いk色の積み上げ棒グラフを作成し，それらをページ順に並べて表示することで，文書内容の可視化を試みる．最後に，実在する小説を対象にして本手法を適用して定性的な分析を行ない，実際の文書内容に対応付けて本手法の彩色結果を説明できることを確認した．

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：Online  

開催年月日： 2020年

記述言語：日本語   会議種別：ポスター発表  

開催地：オンライン  

開催年月日： 2020年

記述言語：日本語   会議種別：ポスター発表  

開催地：Online  

開催年月日： 2020年

記述言語：日本語   会議種別：ポスター発表  

開催地：Online  

開催年月日： 2020年

記述言語：日本語   会議種別：ポスター発表  

開催地：Online  

開催年月日： 2020年

記述言語：英語   会議種別：口頭発表（一般）  

This paper is an extension of our work in developing an attribute-aware semantic segmentation method which focuses on pedestrian understanding in a traffic scene. Recently, the trending topic of semantic segmentation has been expanded to be able to collaborate with the object’s attributes recognition task; Here, it refers to recognizing a pedestrian’s body orientation. The attribute-aware semantic segmentation can be more beneficial for driver assistance compared to the conventional semantic segmentation because it can provide a more informative output to the system. In this paper, we conduct a study of the data augmentation usage as an effort to enhance the performance of the attribute-aware semantic segmentation task. The experiments show that the proposed method in augmenting the training data is able to improve the model’s performance. We also demonstrate some of qualitative results and discuss the benefits to a driver assistance system.

DOI： 10.1109/ICoICT49345.2020.9166219

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：オンライン  

アイドル等の公演では，観客の注目を引き付けるため様々な演出が行なわれており，観客が公演中
に注目している対象の定量的分析は演出の構成を考える上で重要な課題である．そこで本研究では，注視
点と注視対象の動きの類似性を用いて観客の注視対象を判定し，注目度を推定する手法について述べる．
注視対象を判定する手法として，注視点と注視対象の距離で判定する手法が考えられる．しかし，本報告
では群衆を対象とするため，個々の顔画像の解像度は低く，視線推定誤差が大きい．このため，注視対象
間の距離が小さい場合に判定を誤りやすいと考えられる．この問題に対し，動きの類似性を用いて判定す
る手法を提案する．注視対象の判定基準に動きを加えることで，注視対象間の距離によらない判定が可能
になると考えられる．

開催年月日： 2020年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Online  

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2020年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：国立沖縄工業高等専門学校  

近年，高齢化の進展により，独居高齢者に向けた見守りシステムが注目されている．しかし，見守りシステムにはプライバシーの問題が存在する．そこで我々はプライバシーの問題を軽減でき，さらに暗闇での撮影にも強い赤外線センサアレイを用いて撮影した超低解像度FIR画像系列から人物の骨格を推定する手法を提案してきた．本発表では特に超低解像度FIR画像内での人物位置と動作の違いに着目した骨格推定法について検討する．具体的には，超低解像度FIR画像内で人物位置に依存しない特徴を抽出するため，低解像度画像において影響が大きいと考えられる中間層でのPoolingをせずに，特徴抽出の最後にGlobalMaxPoolingを導入する．さらに動作の種類に合わせて，時系列情報と空間的情報を有効活用できるネットワークを提案する．実験では，赤外線センサアレイの画角内の様々な位置で人物を撮影したデータセットを用いて，従来手法と提案手法で特定の人物位置で学習を行ない，未学習位置で骨格推定精度を評価した．その結果，人物の動作を滑らかに推定でき，定量的にも精度が向上することを確認した．

開催年月日： 2020年

記述言語：日本語   会議種別：ポスター発表  

開催地：国立沖縄工業高等専門学校  

近年，自動運転に関する研究開発が活発になされており，限定された環境下ではあるものの公道での走行実験等も進められている．しかし，交差点は事故の危険性が高く，交差点を含む一般道を状況に応じて安全に走 行するための課題は多い．このような背景から，車載カメラを用いて詳細に周囲環境を認識し，周囲の状況を考慮 して安全に走行できる道路上の領域（走路）を推定する技術が求められている．そこで本発表では，車載カメラ画像と，目的地までの経路から得られる車両の進路情報を条件（Condition）とした，conditional GANの枠組みを利用して走行すべき走路を推定する手法を提案する．実走行データから構築したデータセットを用いた評価実験により，画像のみを入力とする比較手法に比べて，提案手法では走路推定精度が15.0%向上することを確認した．

開催年月日： 2020年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Rhodes, Greece (Virtual)  

It is intuitively obvious that eye-gaze behaviors of experienced drivers are different from those of novice drivers. However, it is not easy to understand the difference in their behavior quantitatively. In this work, we present an explainable eye-gaze behavior modeling method for electric wheelchair drivers based on Inverse Reinforcement Learning (IRL). We first create feature maps that represent risk factors during driving. These feature maps are able to represent not only to what but also from where drivers pay attention. IRL uses the feature maps to learn the reward representing the eye-gaze behaviors and allows us to see important features via the automatic acquisition of the reward. Through analysis of the learned model, we show quantitative evidence that eye-gaze behaviors of experienced drivers are better-balanced by paying attention to multiple risks.

DOI： 10.1109/ITSC45102.2020.9294255

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：大分大学  

ロボットが物体を持ち運ぶためには，物体の姿勢を推定する必要があるが，物体が他の物体に遮蔽される場合，観測値の欠損と，観測できた部分を囲む矩形で切り出した距離画像中での物体中心の位置ずれが生じ，姿勢推定が困難である．本発表では，欠損を復元できるよう学習したAutoEncoderにより，位置ずれの補正と欠損の復元をすることで得られる姿勢表現を用いた，遮蔽に頑健な姿勢推定を提案する．

開催年月日： 2019年

記述言語：英語  

開催地：グランキューブ大阪（大阪府立国際会議場）  

Recent multimedia applications commonly use text and imagery from Social Media for tasks related to sentiment research. As such, there are various image datasets for sentiment research for popular classification tasks. However, there has been little research regarding the relationship between the sentiment of images and its annotations from a multi-modal standpoint. In this research, we built a tool to visualize psycholinguistic groundings for a sentiment dataset. For each image, individual psycholinguistic ratings are computed from the image's metadata. A sentiment-psycholinguistic spatial embedding is computed to show a clustering of images across different classes close to human perception. Our interactive browsing tool can visualize the data in various ways, highlighting different psycholinguistic groundings with heatmaps.

開催年月日： 2019年

記述言語：英語   会議種別：ポスター発表  

開催地：Osaka International Convention Center  

Recently, helper robots become popular in our social life, especially for helping the elderly and the disabled people to perform their daily tasks at home. To handling objects, object pose estimation from a depth image is an essential task of the helper robots. However, an object’s pose is often ambiguous from an observation from only a single viewpoint. If we can observe the object from additional viewpoints, the pose estimation result will be better. Thus, we propose a next viewpoint recommendation method based on pose ambiguity minimization. We confirmed and showed the proposed method outperformed other comparative methods on synthetic object images.

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催地：京都大学  

共通の課題に対して複数人で取り組むグループワークは、考え方や能力の異なる他者と協力して問題に取り組む力を育むことから、多くの教育機関で導入・実践が進められている。グループワークにおいては、成果物やテストを通した総括的評価に加え、学習の経過を対象とした形成的評価が重要となる。本発表では、実世界をフィールドとする実世界グループ学習を対象とし、映像・加速度センサ等の複数のデバイスを用いて参加者のグループワーク活動を計測し、それらの可視化を通してグループ学習を分析する取り組みについて紹介する。

開催年月日： 2019年

記述言語：英語   会議種別：ポスター発表  

開催地：Long Beach Convention Center, Long Beach, CA USA  

開催年月日： 2019年

記述言語：日本語   会議種別：ポスター発表  

開催地：パシフィコ横浜アネックスホール  

近年，高齢化社会の進展が問題となっており、高齢者の健康の暮らしのためには身体機能の維持が必須である．そのためには日常的に，身体の動きを把握する必要があるが，センサ装着が必要な方法や可視光カメラを用いる方法ではセンサの装着忘れやプライバシの問題がある．
そこで，超低解像度遠赤外線画像を用いた骨格推定法を提案する．現在，高解像度な可視光画像からの骨格推定は可能であるため，本手法では可視光カメラと赤外線カメラで同時に撮影をするシステムを構築し，学習データを収集する．それを用いて，ニューラルネットワークを学習し，超低解像度遠赤外線画像から直接骨格を推定する．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：国立オリンピック記念青少年総合センター  

車載センサを用いた歩行者検出結果は，実環境において，依然として検出精度に改善の余地がある．
そこで我々は，未検出や誤検出が存在する可能性を，検出器から見たその環境に対する「信頼度」として推定するシステムを提案してきた．
この信頼度を用いることで，各センサの得手・不得手を補った統合検出結果を得ることができると考えられる．
本稿では，各センサにおける入力データから環境を表現する特徴を抽出し，各センサの検出しきい値を出力する複数センサ情報統合器の構築手法について初期検討した結果について報告する．

開催年月日： 2019年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：國立成功大學（臺灣，臺南）  

開催年月日： 2019年

記述言語：英語   会議種別：ポスター発表  

開催地：國立成功大學（臺灣，臺南）  

開催年月日： 2019年

記述言語：英語   会議種別：ポスター発表  

開催地：國立成功大學（臺灣，臺南）  

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：法政大学 市ケ谷キャンパス  

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：早稲田大学 西早稲田キャンパス  

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：早稲田大学 西早稲田キャンパス  

近年，Semantic Segmentationは自動運転や高度運転支援の要素技術として注目を集めている．しかし，高精度なSemantic Segmentation手法の計算コストは高く，実時間処理は困難である．従って，精度を維持したまま，より高速に計算する手法が求められている．Semantic Segmentationの処理は，畳み込み演算の計算時間が大部分を占める．提案手法では，XceptionモジュールのDepthwise convolutionを垂直・水平方向に分解することで畳み込み演算のさらなる高速化を試みる．実験により，mIoUの低下を抑えつつ，パラメータ数と実行時間を削減可能なことを確認した．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：早稲田大学 西早稲田キャンパス  

近年，日常生活の支援のために家庭環境にロボットが導入されつつある．ロボットが物体を持ち運ぶためには，物体の姿勢を推定する必要があるが，物体が密集して存在する場合，他の物体に遮蔽されることで物体画像に欠損が生じ，姿勢推定が困難である．欠損物体を対象とした手法として，欠損を復元するAutoEncoderを学習し，Encoder部分から得られる特徴ベクトルを姿勢推定に用いる手法があるが，この手法は欠損による物体中心の位置ずれを考慮していないため，欠損が大きいほど精度が低い．そこで本発表では，欠損を復元できるように学習したAutoEncoderを用い，欠損復元と位置ずれの修正をしつつ姿勢推定をする手法を提案する．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：早稲田大学 西早稲田キャンパス  

熟練・非熟練ドライバの視行動を比較すると，両者に違いが存在することが知られており，適切な視行動と安全運転には強い相関があると考えられている．これまでに視行動をモデル化する試みは多く行われているが，周囲の幾何的な情報を明示的に考慮した視行動の解析とはなっていない．また，熟練度や経験，また環境変化などに応じて視行動がどのようにモデル化されるかについては，いまだに明らかでない部分が多い．本発表では，潜在的な危険が存在する地点での視行動の形式化の初期検討として，自動車運転のモデル化に向けて，電動車いすを死角あり狭路で操作した場合の視行動を分析した．その結果，習熟するにつれて視線が上昇することを確認した．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：福岡大学七隈キャンパス  

近年，高齢化社会の進展が問題となっており、高齢者の健康の暮らしのためには身体機能の維持が必須である．そのためには日常的に，身体の動きを把握する必要があるが，センサ装着が必要な方法や可視光カメラを用いる方法ではセンサの装着忘れやプライバシの問題がある．
そこで，超低解像度遠赤外線画像を用いた姿勢推定法を提案する．現在，高解像度な可視光画像からの骨格推定は可能であるため，本手法では可視光カメラと赤外線カメラで同時に撮影をするシステムを構築し，学習データを収集する．それを用いて，ニューラルネットワークを学習し，超低解像度遠赤外線画像から直接姿勢を推定する．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：名古屋大学  

開催年月日： 2019年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：名古屋大学  

開催年月日： 2019年

記述言語：日本語   会議種別：ポスター発表  

開催地：北九州国際会議場  

開催年月日： 2019年

記述言語：日本語   会議種別：ポスター発表  

開催地：北九州国際会議場  

本研究では,視覚障害者を支援するために,人物の姿勢変化から歩行者系列が白杖利用者か否か分類す
ることを目的とする.しかし,人物の向きによって画像上の人物姿勢は大きく異なるため,分類しなければなら
ない姿勢パターンが多くなり,分類が困難となる.そこで本発表では,事例ベースで様々な向きの姿勢をある1つ
の向きの姿勢に正規化する手法を提案する.分類する姿勢のパターンが限定されることにより,高精度の分類が
可能となる.評価実験において,提案手法により認識率が 5% 向上することを確認した.

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：北九州国際会議場  

夜間における歩行者の交通死亡事故が多発しており，その原因として運転者による歩行者の視認が困難であることが挙げられる．近年，通常の前方照射に加えて選択的照射が可能なヘッドライトが開発されており，その技術を活かして歩行者に選択的に光を照射することで歩行者の見つけやすさを向上させることが考えられる．我々はこれまでに点滅に着目して効果的な照射法を検討し，特定の周波数において点滅光の照射が効果的であることを確認しているが， 1 つの固定された周辺光条件しか想定していなかった．そこで本研究では，周辺光条件ごとに効果的な点滅照射を分析する．具体的には，運転環境を模擬した臨場感のある仮想環境において 6 種類の周辺光条件を設定し，通常のロービーム照射，ハイビーム照射および 7 種類の点滅周波数を用いて実験を行なった．実験結果から，点滅照射が見つけやすさ向上に有効であることや，異なる周辺光条件では効果的な周波数が異なることを確認した．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：ホテルオークラJRハウステンボス  

旅行を計画する際，満足のいく計画を立てるためには，旅先の雰囲気を予め把握することが重要である．ある地域の訪問予定時の雰囲気が，既に知っている地域のある時期の雰囲気と似ていることが分かれば，その地域の訪問時の雰囲気を直感的に把握することできる．これを実現するため，我々は旅行者の体験を反映した大量のSNS投稿写真に着目した．具体的には，写真に付随する時空間情報を用いて写真をクラスタリングし「時期情報付き地域」を獲得し，写真のVisual Conceptに基づいて時空間類似地域をマイニングすることで，時期を考慮した類似地域の組を獲得する手法を提案する．手法の有用性を示すため，Flickr に投稿された写真を用いて時空間類似地域マイニング実験を行った．また，被験者実験を通して，提案手法の妥当性を確認した．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：ホテルオークラJRハウステンボス  

近年,投稿型料理レシピポータルサイトが普及している.ウェブ上には膨大な数の料理レシピが存在し,
掲載されている料理の種類は多岐にわたる.しかし,それに伴い,サイト利用者が検索の際に目的とする料理レシピ
件数を絞りこむことが難しくなりつつある.そこで本研究では,地域属性(スペイン料理,中華料理など)ごとに料
理の味や見た目が大きく異なることに着目し,料理レシピがもつ多様な情報から,機械学習を用いて作成される料理
の地域属性を推定する手法を提案する.学習には,料理の完成画像から得られた画像特徴,素材一覧から得られた素
材特徴,調理手順文の動詞から得られた手順特徴の 3 つを組み合わせて用いる.結果として,これら 3 つの全特徴を
用いた手法で 8 割近い推定精度が得られることを確認した.

開催年月日： 2019年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Vienna House Diplomat Prague, Prague, Czech Republic  

In recent years, the demand for highly accurate pedestrian detectors has increased due to the development of advanced driving support systems.
For the training of an accurate pedestrian detector, it is important to collect a large number of training samples.
To support this, this paper proposes a ``hard negative'' mining method to automatically extract background images which tend to be erroneously detected as pedestrians.
Negative samples are selected based on the assumption that frequent patterns observed multiple times in the same location are most likely parts of the background scene.
As a result of an evaluation using in-vehicle camera images captured along the same route, we confirmed that the proposed method can automatically collect false positive samples accurately.
We also confirmed that a highly accurate detector can be constructed using the additional negative samples.

開催年月日： 2019年

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Vienna House Diplomat Prague, Prague, Czech Republic  

3D object pose estimation by using a depth sensor is one of the important tasks in activities by robots. To reduce the pose ambiguity of an estimated object pose, several methods for multiple viewpoint pose estimation have been proposed. However, these methods need to select the viewpoints carefully to obtain better results. If the pose of the target object is ambiguous from the current observation, we could not decide where we should move the sensor to set as the next viewpoint. In this paper, we propose a best next viewpoint recommendation method by minimizing the pose ambiguity of the object by making use of the current pose estimation result as a latent variable. We evaluated viewpoints recommended by the proposed method and confirmed that it helps us to gain better pose estimation results than several comparative methods on a synthetic dataset.

開催年月日： 2019年

記述言語：英語   会議種別：ポスター発表  

開催地：Vienna House Diplomat Prague, Prague, Czech Republic  

In recent years, LIDAR is playing an important role as a sensor for understanding environments of a vehicle’s surroundings. Active-scan LIDAR is being actively developed as a LIDAR that can control the laser irradiation direction arbitrary and rapidly. In comparison with conventional uniform-scan LIDAR (e.g. Velodyne HDL-64e), Active-scan LIDAR enables us to densely scan even distant pedestrians. In addition, if appropriately controlled, this sensor has the potential to reduce unnecessary laser irradiations towards non-target objects. Although there are some preliminary studies on pedestrian scanning strategy for Active-scan LIDARs, in the best of our knowledge, an efficient method has not been realized yet. Therefore, this paper proposes a novel pedestrian scanning method based on orientation aware pedestrian likelihood estimation using the orientation-wise pedestrian’s shape models with local distribution of measured points. To evaluate the effectiveness of the proposed method, we conducted experiments by simulating Active-scan LIDAR using point-clouds from the KITTI dataset. Experimental results showed that the proposed method outperforms conventional methods.

開催年月日： 2019年

記述言語：日本語   会議種別：ポスター発表  

開催地：グランキューブ大阪（大阪府立国際会議場）  

近年，先進国を中心に高齢化が進んでおり，電動車いすの需要が高まっている．しかし，電動車いすの利便性は高い一方で，事故が多発している．自動車の事故は視覚的な認知ミスが原因であることが多く，電動車いすも自動車と同様に運転者の視覚的な認知を支援することが事故防止に有効であると考えられる．また，熟練運転者と非熟練運転者の視行動に差異があることが知られており，熟練者の視行動に基づいて，運転者の視覚的な認知の支援をすることができると考えられる．本研究では，電動車いす運転における熟練者の視行動のモデル化を目指し，リスクが複数存在する箇所である死角あり狭路で運転する場合を想定して，熟達に伴う視行動の変化を3次元空間上で分析した．その結果，熟達するほど複数のリスクに対してバランスよく注意を払うことを確認した．

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：広島県情報プラザ  

開催年月日： 2019年

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催地：名古屋大学