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.
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