Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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Similar Seasonal-Geo-Region Mining based on Visual Concepts in Social Media Photos Reviewed

Yasutomo Kawanishi, Ichiro Ide, Chen Lu, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 5th IEEE Conference on Multimedia BigData page： 86 - 93 2019.9

Estimation of the attractiveness of food photography based on image features Reviewed

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

IEICE Trans. on Information and Systems Vol. E102-D ( 8 ) page： 1590 - 1593 2019.8

Hand Orientation Estimation in Probability Density Form

Kazuaki Kondo, Daisuke Deguchi, Atsushi Shimada

2019.6

Estimating the Imageability of a sentence for image caption evaluation

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

2019.4

Estimating the visual variety of concepts by referring to Web popularity Reviewed

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

Multimedia Tools and Applications Vol. 78 ( 7 ) page： 9463 - 9488 2019.4

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

Increasingly sophisticated methods for data processing demand knowledge on the semantic relationship between language and vision. New fields of research like Explainable AI demand to step away from black-boxed approaches and understanding how the underlying semantics of data sets and AI models work. Advancements in Psycholinguistics suggest, that there is a relationship from language perception to how language production and sentence creation work. In this paper, a method to measure the visual variety of concepts is proposed to quantify the semantic gap between vision and language. For this, an image corpus is recomposed using ImageNet and Web data. Web-based metrics for measuring the popularity of sub-concepts are used as a weighting to ensure that the image composition in a dataset is as natural as possible. Using clustering methods, a score describing the visual variety of each concept is determined. A crowd-sourced survey is conducted to create ground-truth values applicable for this research. The evaluations show that the recomposed image corpus largely improves the measured variety compared to previous datasets. The results are promising and give additional knowledge about the relationship of language and vision.

DOI： 10.1007/s11042-018-6528-x

Person Re-Detection from an In-Vehicle Camera Video Referring to Observations from Other Vehicles

Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

The Journal of the Institute of Image Electronics Engineers of Japan Vol. 48 ( 2 ) page： 273 - 277 2019.4

Summarization of multiple news videos considering the consistency of audio-visual contents Reviewed

Ye Zhang, Ryunosuke Tanishige, Ichiro Ide, Keisuke Doman, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

International Journal of Semantic Computing Vol. 13 ( 1 ) page： 135 - 155 2019.3

Hard Negative Mining from in-Vehicle Camera Images based on Multiple Observations of Background Patterns Reviewed

Masashi Hontani, Haruya Kyutoku, David Robert Wong, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2019 Vol. 5 page： 435 - 442 2019.2

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

In recent years, the demand for highly accurate pedestrian detectors has increased due to the development of advanced driving support systems.
For the training of an accurate pedestrian detector, it is important to collect a large number of training samples.
To support this, this paper proposes a ``hard negative'' mining method to automatically extract background images which tend to be erroneously detected as pedestrians.
Negative samples are selected based on the assumption that frequent patterns observed multiple times in the same location are most likely parts of the background scene.
As a result of an evaluation using in-vehicle camera images captured along the same route, we confirmed that the proposed method can automatically collect false positive samples accurately.
We also confirmed that a highly accurate detector can be constructed using the additional negative samples.

Next Viewpoint Recommendation by Pose Ambiguity Minimization for Accurate Object Pose Estimation Reviewed

Nik Mohd Zarifie Hashim, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Ayako Amma, Norimasa Kobori

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2019 Vol. 5 page： 60 - 67 2019.2

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

3D object pose estimation by using a depth sensor is one of the important tasks in activities by robots. To reduce the pose ambiguity of an estimated object pose, several methods for multiple viewpoint pose estimation have been proposed. However, these methods need to select the viewpoints carefully to obtain better results. If the pose of the target object is ambiguous from the current observation, we could not decide where we should move the sensor to set as the next viewpoint. In this paper, we propose a best next viewpoint recommendation method by minimizing the pose ambiguity of the object by making use of the current pose estimation result as a latent variable. We evaluated viewpoints recommended by the proposed method and confirmed that it helps us to gain better pose estimation results than several comparative methods on a synthetic dataset.

Pedestrian Intensive Scanning for Active-scan LIDAR Reviewed

Taiki Yamamoto, Fumito Shinmura, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2019 Vol. 5 page： 313 - 320 2019.2

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

In recent years, LIDAR is playing an important role as a sensor for understanding environments of a vehicle’s surroundings. Active-scan LIDAR is being actively developed as a LIDAR that can control the laser irradiation direction arbitrary and rapidly. In comparison with conventional uniform-scan LIDAR (e.g. Velodyne HDL-64e), Active-scan LIDAR enables us to densely scan even distant pedestrians. In addition, if appropriately controlled, this sensor has the potential to reduce unnecessary laser irradiations towards non-target objects. Although there are some preliminary studies on pedestrian scanning strategy for Active-scan LIDARs, in the best of our knowledge, an efficient method has not been realized yet. Therefore, this paper proposes a novel pedestrian scanning method based on orientation aware pedestrian likelihood estimation using the orientation-wise pedestrian’s shape models with local distribution of measured points. To evaluate the effectiveness of the proposed method, we conducted experiments by simulating Active-scan LIDAR using point-clouds from the KITTI dataset. Experimental results showed that the proposed method outperforms conventional methods.

Voting-based Hand-Waving Gesture Spotting from a Low-Resolution Far-Infrared Image Sequence Reviewed

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

Proceedings of 2018 IEEE International Conference on Visual Communications and Image Processing (VCIP2018) 2018.12

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

We propose a temporal spotting method of a hand gesture from a low-resolution far-infrared image sequence captured by a far-infrared sensor array. The sensor array captures the spatial distribution of far-infrared intensity as a thermal image by detecting far-infrared waves emitted from heat sources. It is difficult to spot a hand gesture from a sequence of thermal images captured by the sensor due to its low-resolution, heavy noise, and varying duration of the gesture. Therefore, we introduce a voting-based approach to spot the gesture with template matching-based gesture recognition. We confirm the effectiveness of the proposed temporal spotting method in several settings.

DOI： 10.1109/VCIP.2018.8698650

Gaze-inspired Learning for Estimating the Attractiveness of a Food Photo Reviewed

Akinori Sato, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of 20th IEEE Int. Symposium on Multimedia (ISM2018) page： 36 - 43 2018.12

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

The number of food photos posted to the Web has been increasing. Most of the users prefer to post delicious-looking food photos. They, however, do not always look delicious. A previous work proposed a method for estimating the attractiveness of food photos, that is, the degree of how much a food photo looks delicious, as an assistive technology for taking a delicious-looking food photo. This method extracted image features from the entire food photo to evaluate the impression. In our work, we conduct a preference experiment where subjects are asked to compare a pair of food photos and measure their gaze. The proposed method extracts image features from local regions selected based on the gaze information and estimates the attractiveness of a food photo by learning regression parameters. Experimental results showed the effectiveness of extracting image features from outside the gaze regions rather than inside them.

DOI： 10.1109/ISM.2018.00015

Action Recognition Using a Far-Infrared Sensor Array by Convolutional RNN Reviewed

Takayuki Kawashima, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa, Masato Kawade

Journal of the Japan Society for Precision Engineering Vol. 84 ( 12 ) page： 1025 - 1032 2018.12

Pedestrian Detection from a Sparse LIDAR Point-Cloud Reviewed

Yoshiki Tatebe, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 84 ( 12 ) page： 1017 - 1024 2018.12

Localizing the Gaze Target of a Crowd of People Reviewed

Yuki Kodama, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hidehisa Nagano, Kunio Kashino

2018.12

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

What target is focused on by many people?
Analysis of the target is a crucial task, especially in a cinema, a stadium, and so on.
However, it is very difficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera.
Therefore, we introduce a new approach for localizing the gaze target focused on by a crowd of people.
The proposed framework aggregates the individually estimated results of each person's gaze.
It enables us to localize the target being focused on by them even though each person's gaze localization from a low-resolution image is inaccurate.
We analyze the effects of an aggregation method on the localization accuracy using images capturing a crowd of people in a tennis stadium under the assumption that all of the people are focusing on the same target, and also investigate the effect of the number of people involved in the aggregation on the localization accuracy.
As a result, the proposed method showed the ability to improve the localization accuracy as it is applied to a larger crowd of people.

Analyzing Headlight Flicker Patterns for Improving the Pedestrian Detectability from a Driver Reviewed

Takashi Maeda, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2018 IEEE 21st International Conference on Intelligent Transportation Systems (ITSC2018) page： 3113 - 3118 2018.11

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

In this paper, we analyze headlight flicker patterns which improve the pedestrian detectability from a driver. Recently, headlights are becoming capable of selectively projecting light on a pedestrian in addition to the normal forward projection. However, it is still not clear how the light should be
projected to effectively improve the visibility of the pedestrian. We actually analyze nine flicker patterns by controlling duty ratios and durations of lighting time, and conduct experiments in field and laboratory settings. As a result, we reveal that a specific fundamental frequency is effective for improving the pedestrian detectability from a driver. We also conclude that the difference between the two settings are not significant.

Estimating the Scene-wise Reliability of LiDAR Pedestrian Detectors Reviewed

Haruya Kyutoku, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Kazuki Kato, Hiroshi Murase

Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) page： 3511 - 3516 2018.11

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

Nowadays, development of driving support systems and autonomous driving systems have become active.
Pedestrian detection from in-vehicle sensors is one of the most important technologies for these systems.
However, outputs of pedestrian detectors can not be fully trusted in real environments.
Therefore, we propose an estimation system of pedestrian detector's reliabilities for a given scene.
This paper proposes a scene-wise reliability calculation method for LiDAR-based detectors, and a construction method for their estimators.
Here, the problem is how we can define the reliability.
The proposed method defines the reliability considering oversights as the strictest threshold without oversights.
Meanwhile, it defines the reliability considering false detections as the loosest threshold without false detections.
Experimental results showed that the proposed method could properly represent the reliability of a given scene, and estimate their reliability.

Attribute-aware Semantic Segmentation of Road Scenes for Understanding Pedestrian Orientations Reviewed

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Jiang-Yu Zheng, Hiroshi Murase

Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) page： 2698 - 2703 2018.11

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

Semantic segmentation is an interesting task for many deep learning researchers for scene understanding. However, recognizing details about objects’ attributes can be more informative and also helpful for a better scene understanding in intelligent vehicle use cases. This paper introduces a method for simultaneous semantic segmentation and pedestrian attributes recognition. A modified dataset built on top of the Cityscapes dataset is created by adding attribute classes corresponding to pedestrian orientation attributes. The proposed method extends the SegNet model and is trained by using both the original and the attribute-enriched datasets. Based on an experiment, the proposed attribute-aware semantic segmentation approach shows the ability to slightly improve the performance on the Cityscapes dataset, which is capable of expanding its classes in this case through additional data training.

Estimation of Driver's Insight for Safe Passing based on Pedestrian Attributes Reviewed

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

Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) page： 1041 - 1046 2018.11

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

In order to reduce traffic accidents between a vehicle and a pedestrian, recognition of a pedestrian who has a possibility of collision with a vehicle should be helpful. However, since a pedestrian may suddenly change his/her direction and cross the road, it is difficult to predict his/her behavior directly. Here, we focus on the fact that experienced drivers usually pass by a pedestrian while preparing to step on the brake at any moment when they feel danger. If driver assistant systems can estimate such experienced driver’s decisions, they could early detect the pedestrian in danger of collision. Therefore, we classify the driver’s decisions into three types by referring to the accelerator operation of drivers, and propose a method to estimate the type of the driver’s decision. The drivers are considered to decide their actions focusing on various behaviors and states of a pedestrian, namely pedestrian’s attributes. Since the driver’s decisions change along the timeline, the use of a temporal context is considered to be effective. Thus, in this paper, we propose an estimation method using a recurrent neural network architecture with the pedestrian’s attributes as input. We constructed a dataset collected by experienced drivers in control of the vehicle and evaluated the performance, and then confirmed the effectiveness of the use of pedestrian’s attributes.

Image Synthesis of Eye Areas for Perceptually Establishing Eye-contacts between Video Conference Participants Reviewed

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

IEEJ Transactions on Electronics, Information and Systems Vol. 138 ( 11 ) page： 1399 - 1409 2018.11

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Papers - Deguchi Daisuke