Feature point detection from point cloud based on repeatability rate and local entropy

Jianjie Wu; Qifu Wang

doi:10.1117/12.751243

15 November 2007 Feature point detection from point cloud based on repeatability rate and local entropy

Jianjie Wu, Qifu Wang

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67865H (2007) https://doi.org/10.1117/12.751243
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Jianjie Wu and Qifu Wang "Feature point detection from point cloud based on repeatability rate and local entropy", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67865H (15 November 2007); https://doi.org/10.1117/12.751243

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