Parsing optical scanned 3D data by Bayesian inference

Hanwei Xiong; Jun Xu; Chenxi Xu; Ming Pan

doi:10.1117/12.2202969

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8 October 2015 Parsing optical scanned 3D data by Bayesian inference

Hanwei Xiong; Jun Xu; Chenxi Xu; Ming Pan

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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 967532 (2015) https://doi.org/10.1117/12.2202969
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

Optical devices are always used to digitize complex objects to get their shapes in form of point clouds. The results have no semantic meaning about the objects, and tedious process is indispensable to segment the scanned data to get meanings. The reason for a person to perceive an object correctly is the usage of knowledge, so Bayesian inference is used to the goal. A probabilistic And-Or-Graph is used as a unified framework of representation, learning, and recognition for a large number of object categories, and a probabilistic model defined on this And-Or-Graph is learned from a relatively small training set per category. Given a set of 3D scanned data, the Bayesian inference constructs a most probable interpretation of the object, and a semantic segment is obtained from the part decomposition. Some examples are given to explain the method.

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Hanwei Xiong, Jun Xu, Chenxi Xu, and Ming Pan "Parsing optical scanned 3D data by Bayesian inference", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967532 (8 October 2015); https://doi.org/10.1117/12.2202969

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