Robust Spatial Autoregressive Modeling for Hardwood Log Inspection

Abstract We explore 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. The application of CT to such industrial vision problems requires efficient and robust image analysis methods. This paper addresses one particular aspect of the problem of creating such a computer vision system, namely, the use of image texture modeling for wood defect recognition. In particular, we contribute the first application of spatial autoregressive (SAR) modeling to wood-grain texture analysis of CT images of hardwood logs. Thereto a circularly shifted correlation approach is developed to discriminate the circular texture patterns on the cross-sectional CT images of logs. A robust algorithm for parameter estimation is applied to obtain model parameters associated with individual defects occurring inside a log. Based on the estimated model features, a simple minimum distance correlation-classifier is constructed which classifies an unknown defect into one of the prototypical defects. Experimental results from the proposed method, applied to CT images from different red oak wood, are given and show the efficacy of our approach.