Bayesian segmentation of range images of polyhedral objects using entropy-controlled quadratic Markov measure field models.

We present a method based on Bayesian estimation with prior Markov random field models for segmentation of range images of polyhedral objects. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well as an improved method for localization of the boundaries between regions. The performance of the method compares favorably with other state-of-the-art procedures when evaluated using a standard benchmark.