2002 Fiscal Year Final Research Report Summary
New Development of Statistical Optimization and Model Selection for Motion Image Analysis
| Project/Area Number |
13680432
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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 |
Intelligent informatics
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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)
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| Project Period (FY) |
2001 – 2002
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| Keywords | 3-D reconstruction / geometric model selection / statistical optimization / gauge theory / multibody motion segmentation / image matching / optical flow / ellipse fitting |
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| Research Abstract |
1. Statistical theory for 3-D analysis of images
We presented a mathematically consistent theory for modeling statistical properties of image noise for analyzing the accuracy of 3-D reconstruction from images, estimating the covariance matrix from the image gray levels, asymptotically evaluating the accuracy of such analysis, and deriving model selection criteria such as "geometric AIC" and "geometric MDL". We also proposed a "gauge theory" for analyzing how the normalization of the reconstructed shape affects its reliability evaluation.
2. Statistical optimization of 3-D reconstruction from images
We built an optimal system for reconstructing 3-D shape from two images and reconstructing 3-D shape from optical flow. We then evaluated and compared their performance, using real images.
3. Automatic correspondence detection between images
We developed a technique for automatically matching feature points independently detected in two images. This is a ulti-stage technique, iteratively upgrading tentative matches incorporating various global constraints. We applied our technique to image mosaicing of multiple images and 3-D reconstruction of scenes and objects.
4. Separation of moving objects in a video sequence
We developed a technique for detecting moving objects in a video sequence in which objects and, backgrounds are both moving independently. We devised a statistical testing method for automatically removing outlying trajectories and a scheme for automatically selecting most appropriate mathematical conditions fo the motion separation. Using real video images, we confirmed that out method was very effective.
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Research Products
(44 results)
