Evolution-based outlier removal for geometric model fitting

Xiong Zhou; Hanzi Wang; Guobao Xiao; Yan Yan; Rui Wang

doi:10.1117/12.2294000

15 November 2017 Evolution-based outlier removal for geometric model fitting

Xiong Zhou, Hanzi Wang, Guobao Xiao, Yan Yan, Rui Wang

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106052O (2017) https://doi.org/10.1117/12.2294000
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

In this paper, we propose a novel method, called Evolution-based Outlier Removal (EOR) method, to remove outliers for robust geometric model fitting. We first select some data points and guide them to evolve towards the inliers. And then, we statistically analyze the evolutional results and distinguish inliers from outliers. Our main contribution in this paper is that, we develop a fitness function to improve the “quality” of selected point sets, which is then used to remove outliers. Experiments on real images illustrate the superiority of the proposed method over several state-of-the-art outlier removal methods.

Citation Download Citation

Xiong Zhou, Hanzi Wang, Guobao Xiao, Yan Yan, and Rui Wang "Evolution-based outlier removal for geometric model fitting", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052O (15 November 2017); https://doi.org/10.1117/12.2294000

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