Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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Typicality analysis of the combination of ingredients in a cooking recipe for assisting the arrangement of ingredients Reviewed

Satoshi Yokoi, Keisuke Doman, Takatsugu Hirayama, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of 2015 IEEE International Conference on Multimedia and Expo (ICME2012) page： 1 - 6 2015.7

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

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.

Pedestrian Orientation Classification Utilizing Single-Chip Coaxial RGB-ToF Camera Reviewed

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hironobu Fujiyoshi

Proceedings of Workshop on Environment Perception for Automated On-road Vehicles in conjunction with 2015 IEEE Intelligent Vehicles Symposium (IV2015) page： 7 - 11 2015.6

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

This paper proposes a method for pedestrian orientation classification. In image recognition, the accuracy is often degraded by the influence of background. In addition, it is also difficult to remove the background and extract only the human body from an image. To overcome these problems, we utilize a single-chip RGB-ToF camera. This camera can acquire RGB and depth images along the same optical axis at the same moment, and thus segmentation of the RGB image becomes easier by using the coaxial depth image. Our proposed method segmented a human body from its background accurately, which lead to the improvement of the accuracy of pedestrian orientation classification.

Translating Apereo Software: A Case Study using Sakai and Transifex

Yuji Tokiwa, Daisuke Deguchi, Juan Jose Merono Sanchez, Jose Mariano Lujan Gonzalez, Diego del Blanco Orobitg

2015.6

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

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

Multi-modal scene duplicate detection from news videos focusing on human faces Reviewed

Haruka Kumagai, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

International Journal of Semantic Computing Vol. 9 ( 2 ) page： 215 - 237 2015.6

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

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

Localization of Open Source Softoware for Higher EducationCommon translation memory Reviewed

Yuji Tokiwa, Daisuke Deguchi, Makoto Miyazaki, Naoshi Hiraoka, Toshihiro Kita, Shoji Kajita

IPSJ Journal of Digital Practices Vol. 6 ( 2 ) page： 79 - 88 2015.4

Adaptive Reference Image Selection for Temporal Object Removal from Frontal In-vehicle Camera Image Sequences Reviewed

Toru Kotsuka, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2015 page： 233 - 239 2015.3

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

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

Estimation of Human Orientation using Coaxial RGB-Depth Images Reviewed

Fumito Shinmura, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hironobu Fujiyoshi

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2015 page： 113 - 120 2015.3

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

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

Scene duplicate detection from news videos using image-audio matching focusing on human faces Reviewed

Haruka Kumagai, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 16th IEEE International Symposium on Multimedia (ISM2014) page： 71 - 77 2014.12

Event detection based on twitter enthusiasm degree for generating a sports highlight video Reviewed

Keisuke Doman, Taishi Tomita, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

page： 949 - 952 2014.11

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

This paper presents a Twitter-based event detection method based on Twitter Enthusiasm Degrees (TED)" toward generating a highlight video of a sports game. Existing methods not only depend on both languages and sports types but also often falsely detect non-target events. In contrast, the proposed method detects sports events using TEDs calculated from several kinds of string features independent of languages and sports. We applied the proposed method to actual sports games, and compared the detected events with the events present in broadcasted highlight videos, and confirmed the effectiveness and the language and sports type independencies of the proposed method."

DOI： 10.1145/2647868.2654973

Human Tracking using a Far-Infrared Sensor Array and a Thermo-Spatial Sensitive Histogram

細野峻司, 高橋友和, 出口大輔, 井手一郎, 村瀬洋, 相澤知禎, 川出雅人

Proceedings of 2nd Workshop on User-Centred Computer Vision 2014.11

Prediction of Driver's Pedestrian Detectability by Image Processing Adaptive to Visual Fields of View Reviewed

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

Proceedings of 2014 IEEE Conference on Intelligent Transportation Systems (ITSC2014) page： 1388 - 1393 2014.10

Vision-based Vehicle Localization using a Visual Street Map with Embedded SURF Scale Reviewed

David Robert Wong, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of Computer Vision in Vehicle Technology with Special Session on Micro Aerial Vehicles (CVVT2014) 2014.9

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

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.

Spatial People Density Estimation from Multiple Viewpoints by Memory Based Regression Reviewed

Yoshimune Tabuchi, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takayuki Kurozumi, Kunio Kashino

Proceedings of the 22nd IAPR International Conference on Pattern Recognition (ICPR2014) page： 2209 - 2214 2014.8

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

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.

DOI： 10.1109/ICPR.2014.384

Single Camera Vehicle Localization Using SURF Scale and Dynamic Time Warping Reviewed

David Robert Wong, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2014 the IEEE Intelligent Vehicles Symposium (IV2014) page： 681 - 686 2014.6

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

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.

Estimation of Traffic Sign Visibility Considering Local and Global Features in a Driving Environment Reviewed

Keisuke Doman, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Utsushi Sakai

Proceedings of 2014 IEEE Intelligent Vehicles Symposium (IV2014) page： 202 - 207 2014.6

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

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.

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

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

DOI： 10.1145/2578726.2578800

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

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Papers - Deguchi Daisuke