| Project/Area Number |
07458067
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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 | GUNMA UNIVERSITY |
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Principal Investigator |
KANATANI Kenichi Gunma University, Faculty of Engineering, Professor, 工学部, 教授 (60125838)
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| Co-Investigator(Kenkyū-buntansha) |
OHTA Naoya Gunma University, Faculty of Engineering, Assistant Professor, 工学部, 助手 (10270860)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥5,100,000 (Direct Cost: ¥5,100,000)
Fiscal Year 1996: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1995: ¥4,300,000 (Direct Cost: ¥4,300,000)
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| Keywords | image processing / image understanding / computer vision / structure from motion / optical flow / 3-D recovery / information criterion / renormalization / 動画像 / ロボットビジョン |
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| Research Abstract |
1.New optical flow detection method
Introducing a precise mathematical model of image noise, we presented a scheme for computing optical flow and evaluating its reliability in quantitative terms.
2.Robust computational scheme for 3-D reconstruction
The 3-D structure is reconstructed if a matrix called the "flow matrix" can be computed by iterations. The convergence of these iterations turned out to be worse for real image data than for simulated date with artificial noise. We presented a new scheme to guarantee the convergence.
3.Mathematical theory of geometric inference from noisy data
It has turned out that the statistical theory that underlines our 3-D analysis of optical flow can be applied to a wide range of general geometric estimation problems involving image and sensor data. We presented a general theory of "optimal fitting", in which a theoretical accuracy bound was derived as a special form of the "Cramer-Rao lower bound".
4.Geometric information criterion
Extending the AIC used in statistics, we defined the "geometric information criterion (AIC)" as a measure for predicting to what extend we could expect robust 3-D reconstruction from noisy image data. We applied it to a various problems including stereo image analysis, motion image analysis and graphic interface, confirmed the effectiveness of our formalism.
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