Automatic feature set selection using the modified Karhunen-Loeve transform: industrial application in visual inspection

For many vision-based inspection tasks, clear measurable features inherent in an image are sufficient to allow classification of the image content. Sometimes, however, it is difficult to select suitable feature sets, as the classification can only be made on the basis of subtle, diffuse relationships within the image. It has previously been shown that it is possible to automatically select sets of 'feature values' in such applications, using a procedure based on a modified version of the Karhunen-Loeve Transform (KLT), applied to window (imagelets) within images. This paper discusses the extension of that work in three directions. It describes the possibilities for using this data- reduction procedure in conjunction with more traditional and better understood classification methods for the decision-making stage. It discusses the potential for application of these ideas by combining the statistical transform coding stage with a range of image pre-processing operations. It also examines some of the issues of industrial integration of this procedure.