<title>Multiscale image segmentation using Gibbs-Markov random fields</title>

Abstract

This paper presents an approach for multiscale image segmentation suitable for applications such as multiscale object recognition. The multiscale segmentation of the input image is obtained by segmenting the scale-space image in a bottom up fashion (i.e., from fine to coarse scale). The segmentation method used combines a Gaussian texture model and Gibbs-Markov contour model to produce an image segmentation which corresponds closely to the objects in the scale-space image. In order to obtain an accurate segmentation at multiple scales, the region labels from the preceding fine scales are propagated as initial conditions for the succeeding coarse scales. Results demonstrate that in general, there is a close similarity between the behavior of the contours derived by this segmentation method, and the behavior of edges found in conventional scale-space approaches. An advantage to this new technique is that the resulting contours are closed, as required by many machine vision algorithms. This is not guaranteed in conventional scale-space methods.