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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Title ( eng )
Linear 3-D object pose estimation with dense sample images : Discussions about limitation of parameter estimation ability by the linear regressions
Creator
Amano Toshiyuki
Source Title
Proceedings of MVA 2009 : IAPR Conference on Machine Vision Applications
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.
NDC
Electrical engineering [ 540 ]
Language
eng
Resource Type conference paper
Publisher
Machine Vision and Application Organization
Date of Issued 2009-05
Rights
Copyright (c) 2009 MVA Conference Committee
Publish Type Version of Record
Access Rights open access