1998 Fiscal Year Final Research Report Summary
Image Recognition and Understanding based on the Geometric Information Criterion
| Project/Area Number |
09680352
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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) |
TAKAHASHI Shigeo Gunma University, Faculty of Engineering, Assistant Professor, 工学部, 助手 (40292619)
OHTA Naoya Gunma University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (10270860)
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| Project Period (FY) |
1997 – 1998
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| Keywords | image processing / geometric AIC / information criterion / optical flow / structure from motion / 3-D reconstruction / CAD / mobile robot |
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| Research Abstract |
1. Self-evaluation from motion images
We have experimentally confirmed that the reliability of 3-D reconstruction from images taken by a moving camera can be evaluated from the original images by using the geometric AIC.
2. Detection of degeneracy
We have experimentally confirmed that in binocular stereo vision and 3-D analysis from two images, the geometric AIC can well distinguish whether the object is a planar surface, the object is located infinitely far away, or the camera is simply rotating without translation.
3. CAD interface
We have experimentally confirmed that regular figures manually input by humans can auto- matically be corrected into intended shapes without introducing empirical thresholds by the use of the geometric AIC.We have also demonstrated that curves and surfaces described by equations can be displayed efficiently by using interval analysis.
4. Moving object detection from motion images
We have experimentally confirmed that we can detect objects moving in the scene by applying the geometric AIC to a image sequence of the scene taken by a moving camera.
5. Rotation estimation
We have developed a technique for optimally computing the 3-D rotation of objects from their noisy stereo images and experimentally confirmed its effectiveness. 6. Robot self-localization We have developed a technique for optimally computing the current position of a mobile robot by matching a noisy image it takes with the 3-D model of the environment and experimentally confirmed its effectiveness.
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