20 March 2008 3D object recognition using fully intrinsic skeletal graphs
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In this paper, we propose a new topology extraction approach for 3D objects. We choose a normalized robust and simplified geodesic-based Morse function to define skeletal Reeb graphs of 3D objects. In addition to scale invariance, we ensure, by using a geodesic distance, the invariance of these graphs to all isometric transforms. In our Reeb graph construction procedure, we introduce important improvements and advantages over existing techniques. We define an efficient sampling rate based on the characteristic resolution intrinsic to each 3D object. Then, we provide a geometry preserving approach by replacing the traditional intervals of a Morse function by its exact level curves. Moreover, we take advantage of the resulting ordered adjacency matrices that describe our Reeb graphs, to introduce a new measure of similarity between the corresponding objects. Experimental results illustrate the computational simplicity and efficiency of the proposed technique for topological Reeb graphs' extraction. The experiments also show the robustness of this approach against noise and object remeshing.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Djamila Aouada, Djamila Aouada, Hamid Krim, Hamid Krim, } "3D object recognition using fully intrinsic skeletal graphs", Proc. SPIE 6814, Computational Imaging VI, 681409 (20 March 2008); doi: 10.1117/12.774868; https://doi.org/10.1117/12.774868


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