<title>Stochastic field-based object recognition in computer vision</title>

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 decribes 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.

Automated analysis of CT images for the inspection of hardwood logs

“…Zhu, Beex, and Conners [10] proposed a stochastic field-based approach for wood texture analysis. In this approach, CT images are first segmented, as described above .…”

“…Several researchers have considered the use of x-ray computed tomography (CT) for this purpose, and have established the feasibility of defect detection using CT imagery [1,2,[8][9][10][11][12]. These researchers have employed texture-based techniques [10], image segmentation methods [11], and knowledge-based classification [9,12] to locate and classify defects. In most cases, image analysis has focused on a single two-dimensional (2D) CT slice, although in a few cases neighboring slices have been used for 3D filtering during preprocessing steps.…”