Defect detection on wooden surface using Gabor filters with evolutionary algorithm design

We apply a model of texture segmentation using multiple spatially and spectrally localized filters, known as Gabor filters, to the analysis of texture and defect regions found on wooden boards. Specifically we present a method to find an optimal set of parameters for a given two-dimensional object detection method. We use feature selection techniques to maximize discrimination. The selection method uses a genetic algorithm to optimize various parameters of the system including Gabor weights, and the parameters of morphological pre-processing. We demonstrate the applicability of the method to the task of classifying wooden textures, and report experimental results using the proposed method.