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Proceedings Paper

Parsing optical scanned 3D data by Bayesian inference
Author(s): Hanwei Xiong; Jun Xu; Chenxi Xu; Ming Pan
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Paper 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.

Paper Details

Date Published: 8 October 2015
PDF: 5 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967532 (8 October 2015); doi: 10.1117/12.2202969
Show Author Affiliations
Hanwei Xiong, Guangdong Univ. of Technology (China)
Jun Xu, Guangdong Univ. of Technology (China)
Chenxi Xu, Guangdong Univ. of Technology (China)
Ming Pan, Guangdong Univ. of Technology (China)

Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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