Faculty Profiles - Deguchi Daisuke

Presentations -

Division display 61 - 80 of about 713 ／ All the affair displays >>

人物姿勢と注視対象配置制約に基づく後ろ向き人物の注視領域推定

弓矢隼大, 川西康友, 出口大輔, 村瀬洋, 細野峻司

第24回画像の認識・理解シンポジウム(MIRU) 2021.7.30

歩き方の特徴に着目した所持物の種類・重さ認識の検討

水野雅也, 川西康友, 出口大輔, 村瀬洋, 井下哲夫

第24回画像の認識・理解シンポジウム(MIRU) 2021.7.29

Pointedness of an Image: Measuring How Pointy an Image is Perceived International conference

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

HCII 2021 2021.7.29

Event date： 2021

Language：English Presentation type：Poster presentation

Venue：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

姿勢変化の連続性に着目した人物姿勢推定器の能動学習

森太郎, 出口大輔, 川西康友, 井手一郎, 村瀬洋, 井下哲夫

第24回画像の認識・理解シンポジウム(MIRU) 2021.7.28

Tell as you imagine: Sentence imageability-aware image captioning International conference

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

27th Int. Conf. on Multimedia Modeling (MMM2021) 2021.6.24

Imageability Estimation using Visual and Language Features International conference

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

2020 International Conference on Multimedia Retrieval 2020.10.28

Event date： 2020

Language：English Presentation type：Poster presentation

Venue：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

A study on image captioning considering its imageability

Kazuki Umemura, Marc Aurel Kastner, Ichiro Ide, Yasutomo Kawanishi, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2020.3.5

Typicality evaluation of food-type specific presentation

Masamu Nakamura, Yasutomo Kawanishi, Keisuke Doman, Takatsugu Hirayama, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2020.3.5

A Preliminary Study on Improving a Cyclist Detector Using Weakly Supervised Learning

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

IEICE General Conference 2020.3.3

A Preliminary Study on Multi-Target Attention Degree Estimation based on the Movement of Gaze Points

Kazuma Takeda, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hidehisa Nagano, Kunio Kashino

IEICE General Conference 2020.3.3

A Preliminary Study on Vehicle Localization Utilizing Multiple Driving Image Sequences

Yuta Goto, Haruya Kyutoku, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

IEICE General Conference 2020.3.3

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

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

International Conference on Computer Vision Theory and Applications (VISAPP) 2020 2020.2.27

Event date： 2020

Language：English Presentation type：Poster presentation

Venue：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.

Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection International conference

Onur Temuroglu, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Hiroshi Murase, Mayuu Iwasaki, Atsushi Tsukada

The International Workshop on Frontiers of Computer Vision (IW-FCV) 2020.2.20

Event date： 2020

Language：English Presentation type：Oral presentation (general)

Venue：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.

Browsing Visual Sentiment Datasets Using Psycholinguistic Groundings International conference

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

Lecture Note in Computer Science 2020.1.4

Event date： 2020

Language：English

Venue：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

More-Natural Mimetic Words Generation for Fine-Grained Gait Description International conference

Hirotaka Kato, Takatsugu Hirayama, Ichiro Ide, Keisuke Doman, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

Lecture Note in Computer Science 2020.1.4

Event date： 2020

Language：English Presentation type：Poster presentation

Venue：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

視覚特徴と言語特徴を用いた単語の心像性推定の検討

松平茅隼, カストナーマークアウレル, 井手一郎, 川西康友, 平山高嗣, 道満恵介, 出口大輔, 村瀬洋

言語処理学会第26回年次大会 2020.3.9

Modeling Eye-Gaze Behavior of Electric Wheelchair Drivers via Inverse Reinforcement Learning International conference

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

2020 IEEE International Conference on Intelligent Transportation Systems 2020.9.21

Event date： 2020

Language：English Presentation type：Oral presentation (general)

Venue：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

A study on a coloring method for document overviews

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

IEICE Technical Report (MVE) 2020.9.9

ソフト順序制約付きラベル境界緩和法に基づく列車前方映像のセマンティックセグメンテーション

振津勇紀, 出口大輔, 川西康友, 井手一郎, 村瀬洋

第23回画像の認識・理解シンポジウム(MIRU) 2020.8.5

移動する複数物体への群衆からの4次元注目度ヒートマップ生成の初期検討

武田一馬, 川西康友, 平山高嗣, 出口大輔, 井手一郎, 村瀬洋, 永野秀尚, 柏野邦夫

第23回画像の認識・理解シンポジウム（MIRU2020） 2020.8.5

　 PREV - NEXT 　

<Deguchi Daisuke>

Presentations -