Combining preference analysis with local constraints for rapid hypothesis generation

Hypothesis generation is crucial to many robust model fitting methods. In this paper, we propose an effective hypothesis generation method by adopting conditional sampling with local constraints. We choose data to generate hypotheses according to sampling weights, which are computed according to ordered residual indices. To sample a minimal subset, we randomly choose a seed datum, compute sampling weights of all data with regard to the seed datum, search the neighborhood set of the seed datum by using the sampling weights, and then sample the remaining data of the minimal subset from the neighborhood set. It has two advantages to consider the neighboring information in guided sampling: It raises the probability of generating all-inlier minimal subsets and it reduces the computational loads in hypotheses generation. The proposed method shows good performance in fundamental matrix estimation using real image pairs.