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Proceedings Paper

Maximum likelihood estimation by random sample and local optimization
Author(s): Wen Tian; Hongyuan Wang; Fan Xu; Qiao Cai
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Paper Abstract

MLESAC is one of the most widely used robust estimators in the field of computer vision. A shortcoming of this method is its low efficiency. An enhancement of MLESAC, the locally optimized MLESAC (LO-MLESAC) is proposed. LO-MLESAC adopts the same sample strategy and likelihood theory as the previous approach and an additional generalized model optimization step is applied to the models with the best quality. Results are given for several image sequences. It is demonstrated that this method gives results superior to original MLESAC.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961F (30 October 2009); doi: 10.1117/12.834458
Show Author Affiliations
Wen Tian, Huazhong Univ. of Science and Technology (China)
Hongyuan Wang, Huazhong Univ. of Science and Technology (China)
Fan Xu, Huazhong Univ. of Science and Technology (China)
Qiao Cai, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision

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