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Proceedings Paper

Stochastic field-based object recognition in computer vision
Author(s): Dongping Zhu; A. A. Beex; Richard W. Conners
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Paper Abstract

This study explores the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. To apply CT to these industrial vision problems requires efficient and robust image analysis methods. The paper addresses one aspect of the problem of creating such a computer vision system, i.e., the issue of statistical image texture modeling for wood defect recognition using a stochastic field-based approach. In particular, it describes a parametric model-based method for studying the spatial stochastic processes -- wood grain textures, with each grain texture being modeled by a parametric random field model. A robust algorithm for parameter estimation is applied to obtain model parameters for individual defects occurring inside a log. By making use of the estimated model features, a simple minimum distance classifier is constructed to classify an unknown defect into one of the prototypical defects. Experimental results of the proposed method with CT images from red oaks are given to show the efficacy of the proposed approach.

Paper Details

Date Published: 1 October 1991
PDF: 8 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48377
Show Author Affiliations
Dongping Zhu, Virginia Polytechnic Institute and State Univ. (United States)
A. A. Beex, Virginia Polytechnic Institute and State Univ. (United States)
Richard W. Conners, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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