Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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A Classification Method of Cooking Operations Based on Temporal Patterns of Gaze Transitions and Blinks Reviewed

Hiroya Inoue, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. J100-A ( 1 ) page： 12 - 23 2017.1

Misclassification Tolerable Learning for Robust Pedestrian Orientation Classification Reviewed

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

page： 481 - 486 2016.12

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

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."

Pedestrian Detection by Scene Adaptation Based on False Positive Mining Reviewed

Yuki Suzuki, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 82 ( 12 ) page： 1085 - 1091 2016.12

Recognition of Texting-While-Walking by Joint Features based on Arm and Head Poses Reviewed

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

2016.11

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

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."

Moving camera background-subtraction for obstacle detection on railway tracks Reviewed

Hiroki Mukoujima, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Masato Ukai, Nozomi Nagamine, Ryuta Nakasone

Proceedings of 2016 IEEE International Conference on Image Processing (ICIP2016) page： 3967 - 3971 2016.9

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

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

Subjective Sensing of Real World Activity on Group Study Reviewed

Daisuke Deguchi, Kazuaki Kondo, Atsushi Shimada

Proceedings of the 18th International Conference on Collaboration Technologies (CollabTech2016) page： 5 - 8 2016.9

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

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.

Proposal of an Encoded Marker for Working Robots Reviewed

Norimasa Kobori, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEEJ Transactions on Electronics, Information and Systems Vol. 136 ( 9 ) page： 1367 - 1375 2016.9

Parts Selective DPM for detection of pedestrians possessing an umbrella Reviewed

Yuto Shimbo, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2016 IEEE Intelligent Vehicles Symposium (IV2016) page： 1053 - 1058 2016.6

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

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."

Using Super-Pixels and Human Probability Map for Automatic Human Subject Segmentation Reviewed

Esmaeil Pourjam, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E99-A ( 5 ) page： 943 - 953 2016.5

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

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

A study on estimating the attractiveness of food photography Reviewed

Kazuma Takahashi, Keisuke Doman, Yasutomo Kawanishi, Takatsugu Hirayama, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of 2016 IEEE Second International Conference on Multimedia Big Data page： 444 - 449 2016.4

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

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.

Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spetrum Reviewed

Brahmastro Kresnaraman, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

Sensors, special issue on "Infrared and THz Sensing and Imaging" Vol. 16 ( 4 ) page： 1 - 16 2016.4

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

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

Subtraction-Based General Forward Obstacle Detection from In-Vehicle Camera Images using Extended Census Transform Reviewed

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

IEEJ Transactions on Electronics, Information and Systems Vol. 136 ( 4 ) page： 588 - 589 2016.4

A classification method of cooking operations based on eye movement patterns Reviewed

Hiroya Inoue, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of Ninth Biennial ACM Symposium on Eye Tracking Research and Applications (ETRA2016) page： 205 - 208 2016.3

HandWaving Gesture Detection Using a Far-Infrared Sensor Array with Thermo-Spatial Region of Interest Reviewed

Chisato Toriyama, Yasutomo Kawanishi, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa, Masato Kawade

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2016 page： 545 - 551 2016.2

Image transformation of eye areas for synthesizing eye-contacts in video conferencing Reviewed

Takuya Inoue, Tomokazu Takahashi, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takayuki Kurozumi, Kunio Kashino

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2016 Vol. 3 page： 273 - 279 2016.2

Human Wearable Attribute Recognition using Decomposition of Thermal Infrared Images Reviewed

Brahmastro Kresnaraman, Yasutomo Kawanishi, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

Proceedings of the 22th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2016) page： 123 - 127 2016.2

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

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.

Human Body Segmentation Using Texture Aware Grab-cut and Statistical Shape Models Reviewed

Esmaeil Pourjam, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 22th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2016) page： 28 - 33 2016.2

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

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.

Human tracking using far-infrared sensor array (in Japanese) Reviewed

Takashi Hosono, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa, Masato Kawade

IEICE Transactions on Information and Systems Vol. J99-D ( 1 ) page： 119 - 129 2016.1

Prediction of driver's pedestrian detectability considering characteristics of human fields-of-view while driving (in Japanese) Reviewed

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

IEICE Transactions on Information and Systems Vol. J99-D ( 1 ) page： 56 - 66 2016.1

Position Interpolation using Feature Point Scale for Decimeter Visual Localization Reviewed

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

Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV2015) Workshops page： 1 - 8 2015.12

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

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.

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Papers - Deguchi Daisuke