Robust Model Fitting Based on Greedy Search and Specified Inlier Threshold

Taotao Lai, Hamido Fujita, Changcai Yang, Qiming Li, Riqing Chen

Abstract: Robust model fitting is an important task for modern electronic industries. In this paper, an efficient robust model-fitting method is proposed to estimate model hypotheses for multistructure data with high outlier rates. The proposed method consists mainly of two steps. First, an improved greedy search strategy is used to generate model hypotheses. Different from the conventional greedy search strategy that always initializes its model hypotheses randomly, the improved greedy search strategy may initialize its model hypotheses by using the inliers of the current best hypotheses for generating more accurate hypotheses. Second, on the basis of the improved greedy search strategy and a specified inlier threshold, a novel parameter detector is used to detect whether the parameters of the generated hypotheses are correct. If they are correct, then the proposed method finishes its fitting process. Otherwise, the first and second steps are performed again. Experimental results on the AdelaideRMF and Hopkins 155 datasets revealed that the proposed method outperformed several state-of-the-art model-fitting methods, including the method based on the conventional greedy search strategy.