Faculty Profiles - Deguchi Daisuke

Presentations -

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過去の車載カメラ映像との時空間差分による不特定障害物検出

久徳遙矢, 出口大輔, 高橋友和, 目加田慶人, 井手一郎, 村瀬洋

画像の認識・理解シンポジウム(MIRU) 2012.8.7

Event date： 2012

Language：Japanese Presentation type：Poster presentation

Venue：福岡国際会議場

本研究の目的は，前方を向いた車載カメラ映像から障害物を検出することである．車載カメラ映像からの障害物検出に関する研究は広く行なわれているが，多くは車両や人物といった特定の検出対象の見えを学習して行なうものであり，事前に学習することができない，段ボール箱や積荷の落下物などの不特定の障害物への対応はできない．そこで提案手法では，同一地点における過去の車載カメラ映像との差分を用いて不特定の前方障害物を検出する．しかし，過去の走行映像は走行位置の違いによる視点のずれを含み，直接的に差分を求めることは困難である．そこで，まず既存手法により現在と過去の映像中のフレーム間対応付けを行なう．そして，対応付いたフレーム同士の道路面を射影変換によって位置合わせする．最後に，対象までの距離を考慮した道路面時空間画像差分により障害物を検出する．評価実験から，ボールなどの不特定の障害物を検出できることを確認した．

Visibility Estimation of Traffic Signals in Rainy Conditions Considering the Visual Noise

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

15th Meeting on Image Recognition & Understanding (MIRU2012) 2012.8.7

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

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

2012 IEEE International Conference on Multimedia and Expo (ICME2012) 2012.7.10

Cook-Log Video Summarization by Removing Temporal Redundancy

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

IEICE Technical Report (DE) 2012.6.6

Construction of a local attraction map according to social visual attention International conference

Ichiro Ide, Jiani Wang, Masafumi Noda, Tomokazu Takahashi, Daisuke Deguchi, Hiroshi Murase

5th International Conference on Intelligent Interactive Multimedia Systems and Services (IIMSS2012) 2012.5

A study on General Obstacle Detection by Spatio-Temporal Subtraction with Past In-Vehicle Camera Image Sequence

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

IEICE Technical Report (PRMU) 2012.3.30

Method for displaying Images on Urban Structures by Augmented Reality

Hiroyuki Uchiyama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takahito Kawanishi, Kunio Kashino

IEICE Technical Report (PRMU) 2012.3.29

A Preliminary Study on Image Features for Visibility Estimation of Traffic Signals in Raining Conditions

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

2012 IEICE General Conference 2012.3.22

A Preliminary Study on the Relationship between Gaze Direction and Head Pose with Respect to Body

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

2012 IEICE General Conference 2012.3.21

A Study on Facial Image Search Using Attributes Based on Impression

Yasunori Ozaki, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

2012 IEICE General Conference 2012.3.21

Super-resolution using facial pose variation

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

2012 IEICE General Conference 2012.3.21

A study on region-based matching for scene duplicate detection from news videos

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

IEICE Technical Report (MVE) 2012.3.12

Digital Camera-Working for Cooking Videos Based on the Determination of Target Areas According to the Cooking Process

Wataru Hyogo, Yasuhiro Hayashi, Masafumi Noda, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (MVE) 2012.3.12

Extraction of Speech Shots Focusing on Visual and Audio Features within and between Shots

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

IEICE Technical Report (MVE) 2012.3.12

Video retrieval by generic object recognition using spatio-temporal CoHOG features

Shogo Nakamura, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

Dynamic Image Processing for Real Application workshop (DIA2012) 2012.3.9

A Study on Image Features Considering Properties of Visual Search for Estimation of Pedestrian Detectability

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

Dynamic Image Processing for Real Application workshop (DIA2012) 2012.3.8

Prediction of driving behavior using gaze information and vehicle information

Tatsuki Kamisaka, Masafumi Noda, Yoshito Mekada, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Dynamic Image Processing for Real Application workshop (DIA2012) 2012.3.8

Integration of generative learning and multiple pose classifiers for pedestrian detection International conference

Hidefumi Yoshida, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Kunihiro Goto, Yoshikatsu Kimura, Takashi Naito

International Conference on Computer Vision Theory and Applications (VISAPP) 2012 2012.2.26

Event date： 2012

Language：English Presentation type：Oral presentation (general)

Venue：Rome, Italy

Recently, pedestrian detection from in-vehicle camera images is becoming an important technology in ITS (Intelligent Transportation System).
However, it is difficult to detect pedestrians stably due to the variety of their poses and their backgrounds.
To tackle this problem, we propose a method to detect various pedestrians from in-vehicle camera images by using multiple classifiers corresponding to various pedestrian pose classes.
Since pedestrians’ pose varies widely, it is difficult to construct a single classifier that can detect pedestrians with various poses stably.
Therefore, this paper constructs multiple classifiers optimized for variously posed pedestrians by classifying pedestrian images into multiple pose classes.
Also, to reduce the bias and the cost for preparing numerous pedestrian images for each pose class for learning, the proposed method employs a generative learning method.
Finally, the proposed method constructs multiple classifiers by using the synthesized pedestrian images.
Experimental results showed that the detection accuracy of the proposed method outperformed comparative methods, and we confirmed that the proposed method could detect variously posed pedestrians stably.

DOI： 10.5220/0003817305670572

Face recognition based on virtual frontal view generation using LVTM with local patches clustering

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

IEICE Technical Report (PRMU) 2012.2.9

Event date： 2012

Language：English Presentation type：Oral presentation (general)

Venue：東北大学

One of the major difficulties encountered by face recognition is the varying poses caused by in-depth rotations.
The intra-person appearance differences caused by rotations are often larger than the inter-person differences, which makes
the traditional face recognition methods such as eigen-face infeasible. This paper presents a framework for face recognition
across pose based on virtual frontal view generation using Local View Transition Model(LVTM) with local patches clustering.
Previous study on LVTM shows that more accurate appearance transition model can be achieved by first dividing the original
face image plane into overlapping local patch regions and then the learned transition models for each patch are aggregated for
the final transformation. In this paper we show that the accuracy the appearance transition model and the recognition rate can
be further improved by better exploiting the inherent linear relationship between frontal-nonfrontal face image pairs. This is
achieved based on the observation that variations in appearance caused by pose are closely related to the corresponding 3D
face structure and intuitively frontal-nonfrontal pairs from more similar local 3D face structures should have a stronger linear
relationship. For each specific location, instead of learning a common transformation as in 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 nonfrontal probe image is transformed using the learned
local view transition model corresponding to the most visually similar cluster. The experimental results on real life face dataset
demonstrate the effectiveness of the proposed method.

A study on a method for high-accuracy detection of a pedestrian holding an umbrella with generative learning

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

IEICE Technical Report (PRMU) 2012.9.3

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