Detecting Correspondences between Video Image Frames and Upgrading Scene Analysis Using Them
| Project/Area Number |
15500113
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | OKAYAMA UNIVERSITY |
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Principal Investigator |
KANATANI Kenichi Okayama University, Department of Information Technology, Professor, 工学部, 教授 (60125838)
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| Co-Investigator(Kenkyū-buntansha) |
SUGAYA Yasuyuki Okayama University, Department of Information Technology, Assistant Professor, 工学部, 助手 (00335580)
剣持 雪子 岡山大学, 工学部, 講師 (60303327)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,800,000 (Direct Cost: ¥3,800,000)
Fiscal Year 2004: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2003: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Keywords | video image processing / image matching / mulitibody motion segmentation / 3-D reconstruction / geometric fitting / geometric model selection / 幾何学的当てはめ / 幾何学的モデル選択 / ロバスト推定 |
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| Research Abstract |
1.Correspondence detection between images
We proposed new methods for automatically extracting feature points in two images and automatically detecting correspondences between them, combining voting schemes based on geometric constraints and global consistency conditions. We supplemented them by devising a technique for generating denser feature correspondences using template matching and a technique for detecting mismatches using tentative 3-D reconstruction.
2.Separation of moving objects in video images
We created a new method for extending the feature point trajectories partially tracked through a video stream by estimating the missing parts. We also created an effective method for separating the tracked trajectories into a background part and an independently moving object part. We also proposed a technique for detecting moving object regions by estimating the motion of the background and subtracting it from the individual video frames.
3.3-D reconstruction from images
We devised robust techniques for 3-D reconstruction from a video stream and from two separate images. We also derived a robust technique for 3-D reconstruction from a single image using knowledge about parallelism and orthogonality relations in the scene. Furthermore, we created a new technique for displaying the reconstructed 3-D shape in such a way that its shape characteristics are preserved.
4.Optimal estimation and model selection for geometric inferences
A considerable progress is made in mathematical analysis of optimal parameter estimation and model selection, and the meaning of the model selection criteria, called the geometric AIC and the geometric MDL, and the theoretical accuracy bound, called the KCR lower bound, that the principal investigator proposed is made clear.
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Report
(3 results)
Research Products
(53 results)
