3D free-form object recognition in range images using local surface patches
DOI: 10.1016/J.PATREC.2007.02.009
关键词: Surface (mathematics) 、 Point (geometry) 、 Feature (computer vision) 、 Object detection 、 Centroid 、 Cognitive neuroscience of visual object recognition 、 Artificial intelligence 、 Mathematics 、 Iterative closest point 、 Histogram 、 Computer vision
摘要: This paper introduces an integrated local surface descriptor for representation and 3D object recognition. A is characterized by its centroid, type a 2D histogram. The histogram shows the frequency of occurrence shape index values vs. angles between normal reference feature point that neighbors. Instead calculating descriptors all points, they are calculated only points in areas with large variation. In order to speed up retrieval deal set objects, patches models indexed into hash table. Given test patches, votes cast containing similar descriptors. Based on potential corresponding candidate hypothesized. Verification performed running Iterative Closest Point (ICP) algorithm align data most likely occurring scene. Experimental results real range presented demonstrate compare effectiveness efficiency proposed approach spin image spherical representations.
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