2000 Fiscal Year Final Research Report Summary
Stable Realization of Virtual Reality by Model Selection
| Project/Area Number |
11680377
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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 | 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) |
HISAMOTO Hiyoshi Gunma University, Faculty of Engineering, Assistant Professor, 工学部, 助手 (40323331)
TAKAHASHI Shigeo Gunma University, Computer Center, Associate Professor, 総合情報処理センター, 助教授 (40292619)
OHTA Naoya Gunma University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (10270860)
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| Project Period (FY) |
1999 – 2000
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| Keywords | 3-D reconstruction / geometric model selection / statistical optimization / model selection criterion / virtual studio / moving camera calibration / image mosaicing / optical flow |
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| Research Abstract |
1.3-D reconstruction from images
We have presented a theory for computing the 3-D structure of the scene and objects from images with maximum possible accuracy and evaluating the reliability of the computed solution in quantitative terms. We have demonstrated the effectiveness of our theory by applying it to 3-D reconstruction from two images and 3-D interpretation of optical flow.
We have also shown how to compute the covariance matrices that characterize the accuracy of feature points extracted from images and experimentally confirmed their positive correlation with their reliability evaluation. We have further analyzed the effects of normalization of the absolute position and the scale for describing the reliability of the computed shape. A new technique for displaying curves and surfaces are also proposed.
We have thus given a theoretical foundation to the reliability evaluation of 3-D reconstruction from images.
2.Virtual reality realization by geometric model selectiony
We have derived the "geometric AIC" and the "geometric MDL" as model selection criteria for geometric inference of noisy data extracted from images and given an information-theoretic interpretation. We have applied these criteria to image mosaicing and demonstrated that the image transformation can be stably computed even if the overlaps between captured images are small.
We have also designed an optimal reference pattern for calibrating a moving camera for virtual studio applications and confirmed by simulations and real-image experiments that the degeneracy of camera configuration and statistical fluctuations of the solution can be avoided by incorporating geometric model selection. Furthermore, we have demonstrated by simulations and real-image experiments that the accuracy of separating independent object motions and estimating the number of objects from their motion images can be dramatically improved by introducing geometric model selection.
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Research Products
(24 results)
