Linear 3-D object pose estimation with dense sample images : Discussions about limitation of parameter estimation ability by the linear regressions
Proceedings of MVA 2009 : IAPR Conference on Machine Vision Applications
Page 287-290
published_at 2009-05
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| File | |
| Title ( eng ) |
Linear 3-D object pose estimation with dense sample images : Discussions about limitation of parameter estimation ability by the linear regressions
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| Creator |
Amano Toshiyuki
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| Source Title |
Proceedings of MVA 2009 : IAPR Conference on Machine Vision Applications
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| Start Page | 287 |
| End Page | 290 |
| Abstract |
In the image parameter estimation by the linear regression, it has very high degrees of freedom for the decision of regression coefficients, because the dimension of image vector is huge high. In this paper, we discuss its potential by the learning of the dense samples. For the learning process, we employed a sequential regression coefficient calculation algorithm and realize its calculation for dense samples with reasonable computational cost. Through the experimental result, we discuss about the limitation of parameter estimation ablity by the linear regression.
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| NDC |
Electrical engineering [ 540 ]
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| Language |
eng
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| Resource Type | conference paper |
| Publisher |
Machine Vision and Application Organization
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| Date of Issued | 2009-05 |
| Rights |
Copyright (c) 2009 MVA Conference Committee
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| Publish Type | Version of Record |
| Access Rights | open access |
