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Proceedings Paper

Feature point detection from point cloud based on repeatability rate and local entropy
Author(s): Jianjie Wu; Qifu Wang
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Paper Abstract

An algorithm to detect feature points directly from unorganized point set is proposed. The algorithm introduces local entropy change of data points on local neighbors as a detection criterion to classify points according to the likelihood that they belong to a feature by making use of the characteristic that local entropy changes sharply in regions where surface changes great. Repeatability rate is introduced as well to reflect the frequency that a sample is detected as a feature point at different local windows. Size of the local neighborhoods is used as a discrete scale parameter to control size of the feature details. Experiment results show that the multi-scale feature point detection can improve the reliability of the detection phase and makes the algorithm more robust in the presence of noise. Furthermore, non-uniformly sampled point cloud can be dealt with.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67865H (15 November 2007); doi: 10.1117/12.751243
Show Author Affiliations
Jianjie Wu, Huazhong Univ. of Science and Technology (China)
Qifu Wang, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)

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