Translator Disclaimer
30 October 2009 Maximum likelihood estimation by random sample and local optimization
Author Affiliations +
Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961F (2009) https://doi.org/10.1117/12.834458
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wen Tian, Hongyuan Wang, Fan Xu, and Qiao Cai "Maximum likelihood estimation by random sample and local optimization", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961F (30 October 2009); https://doi.org/10.1117/12.834458
PROCEEDINGS
6 PAGES


SHARE
Advertisement
Advertisement
Back to Top