Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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Ego-localization by Searching in a Streetview Database using a Sequence of Image Distance based on the Positional Relation between In-vehicle Cameras Reviewed

Haruya Kyutoku, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

IEEJ Transactions on Electronics, Information and Systems Vol. 134 ( 5 ) page： 702 - 710 2014.5

Estimation of the Representative Story Transition in a Chronological Semantic Structure of News Topics Reviewed

Kosuke Kato, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 4th International Conference on Multimedia Retrieval (ICMR2014) page： 487 - 491 2014.4

Language：English Publishing type：Research paper (international conference proceedings)

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

Can a Human be a Sensor? Reviewed

Atsushi Shimada, Daisuke Deguchi, Kazuaki Kondo, Takuya Funatomi

Proceedings of the 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2014) page： 242 - 245 2014.2

Language：English Publishing type：Research paper (international conference proceedings)

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?"."

Environment adaptive pedestrian detection using in-vehicle camera and GPS Reviewed

Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hiroyuki Ishida, Yoshiko Kojima

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2014 page： 354 - 361 2014.1

Language：English Publishing type：Research paper (international conference proceedings)

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

Exemplar-Based Human Body Super-Resolution for Surveillance Camera Systems Reviewed

Kento Nishibori, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2014 page： 115 - 121 2014.1

Language：English Publishing type：Research paper (international conference proceedings)

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

Scene dependent classifiers for pedestrian detection

Hidefumi Yoshida, Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2013.11

Automatic authoring of a domestic cooking video based on the description of cooking instructions Reviewed

Yasuhiro Hayashi, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of 5th International Workshop on Multimedia for Cooking and Eating Activities (CEA'13) page： 21 - 26 2013.10

Blur-invariant Traffic Sign Recognition Using Compact Local Phase Quantization Reviewed

Saleh Aly, Daisuke Deguchi, Hiroshi Murase

Proceedings of 2013 IEEE Conference on Intelligent Transportation Systems (ITSC2013) page： 821 - 827 2013.10

Construction of a traffic sign detector based on voting type co-training Reviewed

Yuji Kojima, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2013 IEEE Conference on Intelligent Transportation Systems (ITSC2013) page： 1137 - 1142 2013.10

Prediction of Pedestrian Detectability for Drivers by Image Processing and its Driver Adaptation Reviewed

Ryunosuke Tanishige, Daisuke Deguchi, Keisuke Doman, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Naoki Nitanda

Proceedings of the 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems (DSP2013) page： 1 - 4 2013.9

Language：English Publishing type：Research paper (international conference proceedings)

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.

Pedestrian Detection by Scene Dependent Classifiers with Generative Learning Reviewed

Hidefumi Yoshida, Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takashi Machida, Yoshiko Kojima

Proceedings of 2013 IEEE Intelligent Vehicles Symposium (IV2013) page： 654 - 659 2013.6

Language：English Publishing type：Research paper (international conference proceedings)

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.

Segmentation of Human Instances Using Grab-cut and Active Shape Model Feedback Reviewed

Esmaeil Pourjam, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of IAPR Conference on Machine Vision Applications (MVA) 2013 page： 77 - 80 2013.5

Cross-pose face recognition Reviewed

Xi Li, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Transaction on Information and Systems, Special Section on Face Perception and Recognition Vol. E96-D ( 3 ) page： 531 - 537 2013.3

Language：English Publishing type：Research paper (scientific journal)

This paper presents an approach for cross-pose face recognition by virtual view generation using an appearance clustering based local view transition model. 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 from training images using partial image in a small region for each location, instead of 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. This is achieved based on the observation that variations in appearance caused by pose are closely related to the corresponding 3D structure and intuitively frontal-nonfrontal patch pairs from more similar local 3D face structures should have a stronger linear relationship. Thus for each specific location, instead of learning a common transformation as in the LVTM, the corresponding local patches are first clustered based on an appearance similarity distance metric and then the transition models are leaned 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.

Two-step super-resolution with binary and grid constraints for low resolution QR-code recognition Reviewed

Yuji Kato, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. J96-D ( 2 ) page： 328 - 337 2013.2

Detection of Biased Broadcast Sports Video Highlights by Attribute-Based Tweets Analysis Reviewed

Takashi Kobayashi, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Lecture Note in Computer Science Vol. 7733 page： 364 - 373 2013.1

Language：English Publishing type：Research paper (international conference proceedings)

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

A Study of Gaze Estimation Using Head and Body Pose Information Reviewed

Nobuhiro Funatsu, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of International Workshop on Advanced Image Technology (IWAIT) 2013 page： 231 - 235 2013.1

Language：English Publishing type：Research paper (international conference proceedings)

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.

Estimation of the human performance for pedestrian detectability based on visual search and motion features Reviewed

Masashi Wakayama, Daisuke Deguchi, Keisuke Doman, Ichiro Ide, Hiroshi Murase, Yukimasa Tamatsu

Proceedings of the 21st IAPR International Conference on Pattern Recognition (ICPR2012) page： 1940 - 1943 2012.11

Language：English Publishing type：Research paper (international conference proceedings)

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.

Virtual View Generation using Clustering based Local View Transition Model Reviewed

Xi Li, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of ACCV Workshop on Face Analysis: The Intersection of Computer Vision and Human Perception 2012.11

Language：English Publishing type：Research paper (international conference proceedings)

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.

Ego-Localization Based on Frame Correspondences between In-Vehicle Camera Images and Streetview Database Reviewed

Haruya Kyutoku, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. J95-D ( 11 ) page： 1973 - 1982 2012.11

Subtraction-Based Forward Obstacle Detection Using Illumination Insensitive Feature for Driving-Support Reviewed

Haruya Kyutoku, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

Proceedings of Computer Vision in Vehicle Technology: From Earth to Mars (CVVT2012) Vol. Part II ( LNCS 7584 ) page： 515 - 525 2012.10

Language：English Publishing type：Research paper (international conference proceedings)

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

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Papers - Deguchi Daisuke