DocumentCode :
3672288
Title :
A convex optimization approach to robust fundamental matrix estimation
Author :
Y. Cheng;J. A. Lopez;O. Camps;M. Sznaier
Author_Institution :
Electr. &
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
2170
Lastpage :
2178
Abstract :
This paper considers the problem of estimating the fundamental matrix from corrupted point correspondences. A general nonconvex framework is proposed that explicitly takes into account the rank-2 constraint on the fundamental matrix and the presence of noise and outliers. The main result of the paper shows that this non-convex problem can be solved by solving a sequence of convex semi-definite programs, obtained by exploiting a combination of polynomial optimization tools and rank minimization techniques. Further, the algorithm can be easily extended to handle the case where only some of the correspondences are labeled, and, to exploit co-ocurrence information, if available. Consistent experiments show that the proposed method works well, even in scenarios characterized by a very high percentage of outliers.
Keywords :
"Polynomials","Optimization","Manganese","Noise measurement","Noise","Sparse matrices","Atomic measurements"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7298829
Filename :
7298829
Link To Document :
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