Presentation | 1997/12/18 Theoretical Accuracy Bounds and Optimal Algorithm in Geometric Estimation Kenichi Kanatani, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper gives a general mathematical framework to the computer vision task of inferring 3-D structures of scenes and objects based on image data and geometric constraints, viewing the problem as model fitting. It can be shown that by modeling the statistical behavior of noise, we can derive a theoretical accuracy bound and a computational scheme that attains that bound. Such a method is truly optimal in the sense that accuracy cannot be improved by any other methods. We illustrate our theory by taking the structure-from-motion problem as an example. Various related topics are also discussed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | optimization / geometric estimation / structure from motion / statistical estimation / theoretical accuracy bound / model fitting |
Paper # | PRMU97-179 |
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Conference Information | |
Committee | PRMU |
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Conference Date | 1997/12/18(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Theoretical Accuracy Bounds and Optimal Algorithm in Geometric Estimation |
Sub Title (in English) | |
Keyword(1) | optimization |
Keyword(2) | geometric estimation |
Keyword(3) | structure from motion |
Keyword(4) | statistical estimation |
Keyword(5) | theoretical accuracy bound |
Keyword(6) | model fitting |
1st Author's Name | Kenichi Kanatani |
1st Author's Affiliation | Department of Computer Science, Gunma University() |
Date | 1997/12/18 |
Paper # | PRMU97-179 |
Volume (vol) | vol.97 |
Number (no) | 458 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |