Presentation 2008-09-05
Unified Computation of Strict Maximum Likelihood for Geometric Fitting
Kenichi Kanatani, Yasuyuki Sugaya,
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Abstract(in English) A new numerical scheme is presented for strictly computing maximum likelihood (ML) of geometric fitting problems. While conventional methods first transform the data into a computationally convenient form and then assume Gaussian noise in the transformed space, our method assumes Gaussian noise in the original data space. It is shown that the strict ML solution can be computed by iteratively using conventional methods. Then, our method is applied to ellipse fitting and fundamental matrix computation. Our method also encompasses optimal correction, computing, e.g., perpendiculars to an ellipse and triangulating stereo images. In the past, such applications have been studied individually. Our method generalizes them from a unified point of view.
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Paper # PRMU2008-50,HIP2008-50
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Committee PRMU
Conference Date 2008/8/29(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Unified Computation of Strict Maximum Likelihood for Geometric Fitting
Sub Title (in English)
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1st Author's Name Kenichi Kanatani
1st Author's Affiliation Department of Computer Science, Okayama University()
2nd Author's Name Yasuyuki Sugaya
2nd Author's Affiliation Department of Information and Computer Sciences, Toyohashi University of Technology
Date 2008-09-05
Paper # PRMU2008-50,HIP2008-50
Volume (vol) vol.108
Number (no) 198
Page pp.pp.-
#Pages 8
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