Presentation | 1994/1/21 Learning and Recognition of 3D Object from Appearance Hiroshi Murase, Shree Nayar, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We address the problem of leaning object models for recognition and pose estimation.We formulate the recognition problem as one of matching visual appearance rather than shape.A new compact image representation called parametric eigenspace is proposed.The image set is compressed to obtain a low-dimensional subspace in which the object is represented as a hypersurface parametrized by pose and illumination.The recognition system projects the image onto the eigenspace.The object is recognized based on the hypersurface it lies on.The position of the projection on the hypersurface determines the object′s pose.We have conducted experiments using s everal objects with complex appearance characteristics. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Object Recognition / Learning / Eigenvector / Pose Estimation / Principal Component Analysis |
Paper # | PRU93-120 |
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Committee | PRU |
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Conference Date | 1994/1/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Understanding (PRU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Learning and Recognition of 3D Object from Appearance |
Sub Title (in English) | |
Keyword(1) | Object Recognition |
Keyword(2) | Learning |
Keyword(3) | Eigenvector |
Keyword(4) | Pose Estimation |
Keyword(5) | Principal Component Analysis |
1st Author's Name | Hiroshi Murase |
1st Author's Affiliation | Basic Research Laboratory,NTT() |
2nd Author's Name | Shree Nayar |
2nd Author's Affiliation | Department of Computer Science,Columbia University |
Date | 1994/1/21 |
Paper # | PRU93-120 |
Volume (vol) | vol.93 |
Number (no) | 431 |
Page | pp.pp.- |
#Pages | 8 |
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