研究者詳細 - 出口　大輔

論文 - 出口　大輔

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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による歩行者に対する安全マージン予測査読有り

前川大和, 平山高嗣, 川西康友, 出口大輔, 劉海龍, 井手一郎, 村瀬洋

電子情報通信学会論文誌 J105-A 巻 ( 3 ) 頁： 38 - 42 2022年3月

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

Masked face recognition with mask transfer and self-attention under the COVID-19 pandemic 査読有り

Zhang Meng, Rujie Liu, Daisuke Deguchi, Hiroshi Murase

IEEE Access 10 巻頁： 20527 - 20538 2022年2月

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

Active Learning for Human Pose Estimation based on Temporal Pose Continuity 査読有り

Taro Mori, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Tetsuo Inoshita

Proceedings of International Workshop on Advanced Image Technology (IWAIT) 2022 2022年1月

Soft-boundary label relaxation with class placement constraints for semantic segmentation of the railway environment 査読有り

Yuki Furitsu, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Hiroki Mukojima, Nozomi Nagamine

Pattern Recognition Letters 150 巻頁： 258 - 264 2021年10月

Pointedness of an Image: Measuring How Pointy an Image is Perceived 査読有り

Chihaya Matsuhira, Marc A. Kastner, Ichiro Ide, Yasutomo Kawanishi, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

Proceedings of HCII 2021 37 巻頁： 137 - 144 2021年7月

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

Best next-viewpoint recommendation by selecting minimum pose ambiguity for category-level object pose estimation 査読有り

Nik Mohd Zarifie Hashim, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering 87 巻 ( 5 ) 頁： 440 - 446 2021年5月

画像特徴点の対応を制約とした疎なLiDAR点群からのOdometry推定査読有り

清水政行, 川西康友, 出口大輔, 井手一郎, 村瀬洋

精密工学会誌 87 巻 ( 5 ) 頁： 447 - 454 2021年5月

光線方向の違いを考慮したライトフィールド特徴量の提案査読有り

清水政行, 川西康友, 出口大輔, 井手一郎, 村瀬洋

精密工学会誌 87 巻 ( 2 ) 頁： 197 - 205 2021年2月

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

ColAtt-Net: In Reducing the Ambiguity of Pedestrian Orientations on Attribute-Aware Semantic Segmentation Task 査読有り

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Takatsugu Hirayama, Hiroshi Murase

IEEJ Transactions on Electrical and Electronic Engineering 16 巻 ( 2 ) 頁： 295 - 306 2021年2月

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

LFIR2Pose: Pose Estimation from an Extremely Low-Resolution FIR Image Sequence 査読有り

Saki Iwata, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa

Proceedings of the 25th International Conference on Pattern Recognition 2021年1月

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.

OMEga-GAN: Object Manifold Embedding GAN for Image Generation by Disentangling Parameters into Pose and Shape Manifolds 査読有り

Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of the 25th International Conference on Pattern Recognition 頁： 7945 - 7952 2021年1月

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.

Median-Shape Representation Learning for Category-Level Object Pose Estimation in Cluttered Environments 査読有り

Hiroki Tatemichi, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Ayako Amma, Hiroshi Murase

Proceedings of the 25th International Conference on Pattern Recognition 頁： 4473 - 4480 2021年1月

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.

超低解像度FIR画像系列中での人物位置と行動の違いに着目した骨格推定法査読有り

岩田紗希, 川西康友, 出口大輔, 井手一郎, 村瀬洋, 相澤知禎

精密工学会誌 87 巻 ( 1 ) 頁： 99 - 106 2021年1月

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.

Imageability Estimation using Visual and Language Features 査読有り

Chihaya Matsuhira, Marc A. Kastner, Ichiro Ide, Yasutomo Kawanishi, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 2020 International Conference on Multimedia Retrieval 頁： 306–310 2020年10月

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

Modeling Eye-Gaze Behavior of Electric Wheelchair Drivers via Inverse Reinforcement Learning 査読有り

Yamato Maekawa, Naoki Akai, Takatsugu Hirayama, Luis Yoichi Morales, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of 2020 IEEE International Conference on Intelligent Transportation Systems 頁： 158 - 164 2020年9月

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

Estimating the imageability of words by mining visual characteristics from crawled image data 査読有り

Marc Aurel Kastner, Ichiro Ide, Frank Nack, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Hiroshi Murase

Multimedia Tools and Applications 79 巻 ( 25 ) 頁： 18167 - 18199 2020年7月

Performance Boost of Attribute-aware Semantic Segmentation via Data Augmentation for Driver Assistance 査読有り

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Takatsugu Hirayama, Hiroshi Murase

頁： 293 - 298 2020年6月

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

車両部位検出に基づく回帰を利用した車両台数の推定査読有り

新村文郷, 川西康友, 出口大輔, 井手一郎, 村瀬洋

知能と情報 32 巻 ( 3 ) 頁： 705 - 712 2020年6月

Simultaneous Image Matching for Person Re-identification via the Stable Marriage Algorithm 査読有り

Nik Mohd Zarifie Hashim, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEEJ Transactions ON Electrical AND Electronic Engineering 15 巻 ( 6 ) 頁： 909 - 917 2020年4月

SOANets: Encoder-Decoder based Skeleton Orientation Alignment Network for White Cane User Recognition from 2D Human Skeleton Sequence 査読有り

Naoki Nishida, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Jun Piao

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2020 5 巻頁： 435 - 443 2020年2月

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.

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