Presentation	2006-03-17 High Accuracy Fundamental Matrix Computation and Its Performance Evaluation Yasuyuki Sugaya, Kenichi Kanatani,
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Abstract(in English)	This paper studies numerical schemes of maximum likelihood estimation for computing the fundamental matrix from feature point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods, FNS, HEIV, and renormalization, to which we add a new algorithm based on Gauss-Newton iterations. Using simulated images, we compare their convergence properties. The initial value is chosen in three ways: randomly, by least-squares, and by the Taubin method. We also show real image experiments. These experiments reveal characteristics of each method. It is concluded that FNS has the best convergence properties.
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Paper #	PRMU2005-264
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Committee	PRMU
Conference Date	2006/3/10(1days)
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Registration To	Pattern Recognition and Media Understanding (PRMU)
Language	JPN
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Title (in English)	High Accuracy Fundamental Matrix Computation and Its Performance Evaluation
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1st Author's Name	Yasuyuki Sugaya
1st Author's Affiliation	Department of Computer Science, Okayama University()
2nd Author's Name	Kenichi Kanatani
2nd Author's Affiliation	Department of Computer Science, Okayama University
Date	2006-03-17
Paper #	PRMU2005-264
Volume (vol)	vol.105
Number (no)	674
Page	pp.pp.-
#Pages	8
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