| Project/Area Number |
04650378
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
計測・制御工学
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| Research Institution | Osaka University |
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Principal Investigator |
MIYAZAKI Fumio Osaka Univ., Fac. of Eng. Science, Professor, 基礎工学部, 教授 (20133142)
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| Co-Investigator(Kenkyū-buntansha) |
MASUTANI Yasuhiro Osaka Univ., Fac. of Eng. Science, Res. Associate, 基礎工学部, 助手 (80219328)
MARU Noriaki Osaka Univ., Fac. of Eng. Science, Res. Associate, 基礎工学部, 助手 (60239150)
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| Project Period (FY) |
1992 – 1993
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| Project Status |
Completed (Fiscal Year 1993)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1993: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1992: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Binocular Stereo / Active Motion / Binocular Disparity / Motion Parallax / Occlusion / Occuding Contour |
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| Research Abstract |
This reseatch investigated the active stereo vision system to acquire the reliable 3D information of the environment efficiently. We debeloped the hardware and software of the Bision Robot which can control the movement of the stereo camera. We proposed an active method which can detect both raliable binocular disparities and occlusion efficiently, because the method solves the correspondence problem with out using the heuristic constraints. Furthermore, we extended the method so as to deal with the more complicated scene shich contains the curved objects. In general, the projected image of the bundary of the curved object is called Occluding Contour shich does not exist in physical. The conventional stereo method can not deal with the scene containing the curved objects, because the matching of these occluding contour produce the error in 3D information. We proposed a reliable method to hetect occluding contours and occlusion by active binocular stereo with an occluding contour model that describes the relation between the cotour of curved object and its projected image. Applying the image flow generated by moving one camera to the model, we can restrict possible matched points seen by the other camera. We detect occluding contours by fitting these matched points to the model. This method can find occluding contours and occlusion more reliably than conventional ones because stereo matching is performed by using the geometric constraint based on the occluding contour model instead of heuristic constraints.
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