Maximum Likelihood Estimation Sample Consensus with Validation of Individual Correspondences

Authors:
Liang Zhang;Houman Rastgar;Demin Wang;André Vincent
Affiliations:
Communications Research Centre Canada, Ottawa, Canada K2H 8S2;Communications Research Centre Canada, Ottawa, Canada K2H 8S2;Communications Research Centre Canada, Ottawa, Canada K2H 8S2;Communications Research Centre Canada, Ottawa, Canada K2H 8S2
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

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Abstract

This paper presents an extension of the maximum likelihood estimation sample consensus (MLESAC) by introducing an online validation of individual correspondences, which is based on the Law of Large Numbers (LLN). The outcomes of the samples, each considered a random event, are analyzed for useful information regarding the validities of individual correspondences. The information from the individual samples that have been processed is accumulated and then used to guide subsequent sampling and to score the estimate. To evaluate the performance of the proposed algorithm, the proposed method was applied to the problem of estimating the fundamental matrix. Experimental results with the Oxford image sequence, Corridor , showed that for a similar consensus the proposed algorithm reduced, on average, the Sampson error by about 13% and 12% in comparison to the RANSAC and the MLESAC estimator, while the associated number of samples decreased by about 14% and 15%, respectively.