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Rapid hypothesis generation by combining residual sorting with local constraints

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Abstract

Efficient hypothesis generation plays an important role in robust model fitting. In this study, based on the combination of residual sorting and local constraints, we propose an efficient guided hypothesis generation method, called Rapid Hypothesis Generation (RHG). By exploiting the local constraints to guide the hypothesis generation process, RHG raises the probability of generating promising hypotheses and reduces the computational cost during hypotheses generation. Experimental results on homography and fundamental matrix estimation show that RHG can effectively guide hypothesis generation process and rapidly generate promising hypotheses for heavily contaminated multi-structure data.

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  1. http://cs.adelaide.edu.au/~hwong/doku.php?id=data

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Acknowledgments

The authors want to thank Dr. Tat-Jun Chin of Adelaide university for providing some competing methods. This work was supported by the National Natural Science Foundation of China under Grants 61472334, 61305004, and 61571379.

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Correspondence to Hanzi Wang.

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Lai, T., Wang, H., Yan, Y. et al. Rapid hypothesis generation by combining residual sorting with local constraints. Multimed Tools Appl 75, 7445–7464 (2016). https://doi.org/10.1007/s11042-016-3365-7

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