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Proceedings Paper

Evolution-based outlier removal for geometric model fitting
Author(s): Xiong Zhou; Hanzi Wang; Guobao Xiao; Yan Yan; Rui Wang
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Paper 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.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052O (15 November 2017); doi: 10.1117/12.2294000
Show Author Affiliations
Xiong Zhou, Xiamen Univ. (China)
Hanzi Wang, Xiamen Univ. (China)
Guobao Xiao, Xiamen Univ. (China)
Yan Yan, Xiamen Univ. (China)
Rui Wang, Institute of Information Engineering (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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