A New Bayesian Method for Range Image Segmentation

Abstract

In this paper we present and evaluate a new Bayesian method for range image segmentation. The method proceeds in two stages. First, an initial segmentation is produced by a randomized region growing technique. The produced segmentation is considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, pixels not labeled in the first stage are labeled by using a Bayesian estimation based on some prior assumptions on the regions of the image. The image priors are modeled by a new Markov Random Field (MRF) model. Contrary to most of the authors in range image segmentation, who use only surface smoothness MRF models, our MRF takes into account also the smoothness of region boundaries. Tests performed with real images from the ABW database show a good potential of the proposed method for significantly improving the segmentation results.