In past few years, robust statistical methods have significantly contributed to the advances made in computer vision. In particular, the class of random-sample consensus, or RANSAC type algorithms for the solution of problems in robust estimation has been key to progress.
The fitting problem is approached in the opposite way from most previous techniques, such as the least squares approaches. Instead of averaging all the measurements and then trying to throw out bad ones, the smallest number of measurements is used to compute a model’s unknown parameters and then evaluated the instantiated model by counting the number of consistent samples.
I will review the RANSAC technique and some of its variants/applications, as well as the future research directions.
CVPR 2006 Workshop: 25 Years of RANSAC - http://cmp.felk.cvut.cz/ransac-cvpr2006/
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