Stochastic field-based object recognition in computer vision

Dongping Zhu; A. A. Beex; Richard W. Conners

doi:10.1117/12.48377

1 October 1991 Stochastic field-based object recognition in computer vision

Dongping Zhu, A. A. Beex, Richard W. Conners

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48377
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Dongping Zhu, A. A. Beex, and Richard W. Conners "Stochastic field-based object recognition in computer vision", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48377

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