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Proceedings Paper

Induced subgraph searching for geometric model fitting
Author(s): Fan Xiao; Guobao Xiao; Yan Yan; Xing Wang; Hanzi Wang
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Paper Abstract

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the “qualified” subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

Paper Details

Date Published: 15 November 2017
PDF: 8 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054B (15 November 2017); doi: 10.1117/12.2296331
Show Author Affiliations
Fan Xiao, Xiamen Univ. (China)
Guobao Xiao, Xiamen Univ. (China)
Yan Yan, Xiamen Univ. (China)
Xing Wang, Xiamen Univ. (China)
Hanzi Wang, Xiamen Univ. (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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