Performance evaluation of a segmentation algorithm for synthetic texture images

In this paper we present the performance evaluation of a texture segmentation approach for synthetic textured images. Our segmentation approach uses a Bayesian inference procedure using co-ocurrence properties over a set of randomly sampled points in the image. We developed an exhaustive performance test for this approach that compares segmentation results to the “ground truth” images under a varying number of sampled points, in the neighborhood of each pixel used to classify it in the test images. We show our preliminary results that let us to choose the optimal number of points to analyze in the neighborhood of each pixel to assign a texture label. This method can be easily applied to segment outdoor real textured images.