| Project/Area Number |
14380161
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
SUGIMOTO Akihiro National Institute of Informatics, Intelligent Systems Research Division, Associate Professor, 知能システム研究系, 助教授 (30314256)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUYAMA Takashi Kyoto University, Graduate School of Informatics, Professor, 大学院・情報学研究科, 教授 (10109035)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥15,100,000 (Direct Cost: ¥15,100,000)
Fiscal Year 2004: ¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 2003: ¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2002: ¥8,700,000 (Direct Cost: ¥8,700,000)
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| Keywords | wearable vision sensor / shape reconstruction / surface reflectance / 3D image registration / motion estimation / computer vision / wearable vision / 装着型視角センサ / 視線情報 / ウェアラブルビジョン |
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| Research Abstract |
To establish man-machine interactions in the wearable computer environments, we studied reconstructing the shape and texture of a 3D object that a user takes in his hands through the active wearable vision sensor information. Obtained research results can be summarized as follows.
1.Detecting blink points of a person using active wearable vision sensor.
We developed a wearable vision sensor that consists of the detector of person's viewing lines and two active cameras. First, we established a method for calibrating the sensor so that it can detect person's blink points accurately even in a real situation such that the depth of blink points changes. Next, we proposed a method for detecting a gazing region of a person.
2.Shape reconstruction of a hand-held 3D object.
We proposed a registration method for range images that preserves local structures of object surfaces. The method introduces shape patterns and a skewness of correspondences, both of which are extracted from the local surface ne
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arby a point of interest in each image. These two features enable us to reconstruct full shape of a 3D object that preserves local structures of object surfaces.
3.Reflectance estimation of a 3D object under complex illumination.
We developed a method for recovering reflectance properties of a moving Lambertian object from an image sequence of the object taken by a fixed camera under unknown and complex illumination. Our method enables us to synthesize realistic images of the object in arbitrary poses under arbitrary lighting conditions by using the reconstructed shape and albedo.
4.Ego-motion estimation using active wearable vision sensor.
We proposed a method for incrementally estimating ego motion by two mounted active cameras. Our method independently controls the two cameras during the ego motion so that each camera automatically fixates its optical axisto its own fixation point. Two cameras do not have to share the common field of view in this case whereas in stereo vision they have to do. Less
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