Fast Texture-Based Tracking and Delineation Using Texture Entropy

Ali Shahrokni, Tom Drummond, Pascal Fua

Abstract: We propose a fast texture-segmentation approach to the problem of 2D and 3D model-based contour tracking, which is suitable for real-time or interactive applications. Our approach relies on detecting texture boundaries in the direction normal to the contour boundaries and on using a hidden Markov model to link these boundary points in the other direction. The probabilities that appear in this computation closely relate to texture entropy and Kullback-Leibler divergence, a property we use to compute and update dynamic texture models. We demonstrate results both in the context of interactive 2D delineation and fast 3D tracking