| Project/Area Number |
12650392
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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 |
情報通信工学
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| Research Institution | Keio University |
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Principal Investigator |
OZAWA Shinji Keio University, Department of Information and Computer Science, Professor, 理工学部, 教授 (70051761)
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| Co-Investigator(Kenkyū-buntansha) |
SAITO Hideo Keio University, Department of Information and Computer Science, Associate Professor, 理工学部, 助教授 (90245605)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 2001: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2000: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | Shape Recovery / 3D Model / Super Resolution / Mosaicing / Factorization Method / Computer Vision / Moving Camera |
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| Research Abstract |
In this research, we aim to develop a method for synthesizing super-resolved images, that improve spatial resolution of input images by merging multiple input images taken by a moving camera. When a moving camera5takes image sequence, some part of image is overlapped in different frames. In such overlapped part, feature points are detected for obtaining point-by-point correspondence between the frames. This correspondence information provides shape of the object by applying Factorization method. Although the execution of Factorization method only provides a part of the object shape, a number of the part of the shape are merged for generating whole shape. The input images are rendered onto the generated object shape, so that the 3D model with texture can be reconstructed. Since a number of images can be rendered onto the same surface of the object, spatial resolution can be improved. The generated images projected from the reconstructed 3D model with texture can be regarded as mosaic image with super resolution. In the experiment performed in this research project, we can generate super resolved images that have 4-16 times spatial resolution.
We also propose a novel method for synthesizing super-resolved images from uncalibrated moving camera by using projective geometry. In this method, point-by-point correspondence between input frames can be generated by fundamental matrices that are derived from the projective geometry. For minimizing the corresponding error, we also implement an optimization technique, in which higher frequency component of the image is estimated for the optimization evaluation. The experimental results demonstrate the efficacy of the method.
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