| 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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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥4,200,000 (Direct Cost: ¥4,200,000)
Fiscal Year 2002: ¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2001: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Keywords | 3-D reconstruction / geometric model selection / statistical optimization / gauge theory / multibody motion segmentation / image matching / optical flow / ellipse fitting / 幾何学モデル選択 / ビデオ画像処理 / ロバスト推定 / 幾何学的信頼性評価 / 動画像解析 / モデル選択規準 |
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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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