Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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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 Open Access

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

Visibility Estimation of Traffic Signals under Rainy Weather Conditions for Smart Driving Support Reviewed

Ryuhei Sato, Keisuke Doman, Daisuke Deguchi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Yukimasa Tamatsu

Proceedings of 2012 IEEE Conference on Intelligent Transportation Systems (ITSC2012) page： 1321 - 1326 2012.9

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

The aim of this work is to support a driver by
notifying the information of traffic signals in accordance with
their visibility. To avoid traffic accidents, the driver should
detect and recognize surrounding objects, especially traffic
signals. However, when driving a vehicle under rainy weather
conditions, it is difficult for drivers to detect or to recognize
objects existing in the road environment in comparison with fine
weather conditions. Therefore, this paper proposes a method
for estimating the visibility of traffic signals for drivers under
rainy weather conditions by image processing. The proposed
method is based on the concept of visual noise known in the
field of cognitive science, and extracts two types of visual noise
features which ware considered that they affect the visibility of
traffic signals. We expect to improve the accuracy of visibility
estimation by combining the visual noise features with the
texture feature introduced in a previous work. Experimental
results showed that the proposed method could estimate the
visibility of traffic signals more accurately under rainy weather
conditions.

DOI： 10.1109/ITSC.2012.6338838

Detection and classification of repetitious human motions combining shift variant and invariant features Reviewed

Ichiro Ide, Taku Kuhara, Daisuke Deguchi, Tomokazu Takahashi, Hiroshi Murase

Proceedings of the 3rd International Conference on Emerging Security Technologies (EST2012) page： 86 - 89 2012.9

Robust Face Super-Resolution Using Free-Form Deformations For Low-Quality Surveillance Video Reviewed

Tomonari Yoshida, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2012 IEEE International Conference on Multimedia and Expo (ICME2012) page： 368 - 373 2012.7

Speech shot extraction from broadcast news videos Reviewed

Shogo Kumagai, Keisuke Doman, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

International Journal of Semantic Computing Vol. 6 ( 2 ) page： 179 - 204 2012.6

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

We propose a method for discriminating between a speech shot and a narrated shot to extract genuine speech shots from a broadcast news video. Speech shots in news videos contain a wealth of multimedia information of the speaker, and could thus be considered valuable as archived material. In order to extract speech shots from news videos, there is an approach that uses the position and size of a face region. However, it is difficult to extract them with only such an approach, since news videos contain non-speech shots where the speaker is not the subject that appears in the screen, namely, narrated shots. To solve this problem, we propose a method to discriminate between a speech shot and a narrated shot in two stages. The first stage of the proposed method directly evaluates the inconsistency between a subject and a speaker based on the co-occurrence between lip motion and voice. The second stage of the proposed method evaluates based on the intra- and inter-shot features that focus on the tendency of speech shots. With the combination of both stages, the proposed method accurately discriminates between a speech shot and a narrated shot. In the experiments, the overall accuracy of speech shots extraction by the proposed method was 0.871. Therefore, we confirmed the effectiveness of the proposed method.

DOI： 10.1142/S1793351X12400077

Real-time marker-free patient registration for electromagnetic navigated bronchoscopy: a phantom study Reviewed

Daisuke Deguchi, Marco Feuerstein, Takayuki Kitasaka, Yasuhito Suenaga, Ichiro Ide, Hiroshi Murase, Kazuyoshi Imaizumi, Yoshinori Hasegawa, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 7 ( 3 ) page： 359 - 369 2012.5

Authorship：Lead author Language：English Publishing type：Research paper (scientific journal)

Purpose
To perform bronchoscopy safely and smoothly, it is very important to develop a bronchoscopic guidance system. Transbronchial lung biopsy (TBLB) with a bronchoscopic guidance system especially should permit safe image-guided procedures. Recently, electromagnetic tracking (EMT) is utilized to track the tip of the bronchoscope camera in real time. For most tracking methods using position sensors, registration between tracking data and previously acquired reference image data, such as CT image, is performed using natural landmarks of the patient or fiducial markers attached to the patient, whose positions need to be measured manually by the physician before the actual bronchoscopy. Therefore, this paper proposes a marker-free CT-to-patient registration method utilizing bronchoscope's position and orientation obtained by the EMT.

Methods
We developed a guidance system that is able to track the tip of the bronchoscope camera in real time. In the case of a guidance system that uses position sensors, natural landmarks of the patient or fiducial markers attached to the patient are needed to obtain the correspondence between EMT outputs and previously acquired reference image data, such as CT image. This paper proposes a registration method without landmarks or fiducials by estimating the transformation matrix between the patient and the CT image taken prior to the bronchoscopic examination. This estimation is performed by computing correspondences between the outputs of the EMT sensor and airways extracted from the CT image. As ambiguities between EMT measurements and their corresponding airway branches may arise at airway bifurcations, we introduce a stable airway branch selection mechanism for improving the robustness of the estimation of the transformation matrix. To evaluate the performance of the proposed method, we applied the method to a rubber bronchial phantom and added virtual breathing motion to the sensor output.

Results
Experimental results show that the accuracy of our proposed method is within 2.0-3.0 mm (without breathing motion) and 2.5-3.5 mm (with breathing motion). The proposed method could also track a bronchoscope camera in real time.

Conclusions
We developed a method for CT-to-patient registration using a position sensor without fiducial markers and natural landmarks. Endoscopic guided biopsy of lung lesions is feasible using a marker-free CT-to-patient registration method.

DOI： 10.1007/s11548-011-0626-9

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Papers - Deguchi Daisuke