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Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal or elemental subsets of the data, and hypothesize the geometric model from the selected subsets. While taking minimal subsets increases the chance of simultaneously "hitting" inliers in a sample, it amplifies the noise of the underlying model, and hypotheses fitted on minimal subsets may be severely biased even if they contain purely inliers. Indoi:10.1109/cvpr.2012.6247740 dblp:conf/cvpr/PhamCYS12 fatcat:iai2mtv3kzemxevqubky6wolhe