Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach

Abstract

Abstract. We p r o p o s e a v ector representation approach t o c o n tour estimation from noisy data. Images are modeled as random elds composed of a set of homogeneous regions contours (boundaries of homogeneous regions) are assumed to be vectors of a subspace of L 2 (T ) generated by a g i v en nite basis B-splines, Sinc-type, and Fourier bases are considered. The main contribution of the paper is a smoothing criterion, interpretable as a priori contour probability, based on the Kullback distance between neighboring densities. The maximum a posteriori probability (MAP) estimation criterion is adopted. To solve the optimization problem one is led to (joint estimation of contours, subspace dimension, and model parameters), we propose a gradient projection type algorithm. A set of experiments performed on simulated an real images illustrates the potencial of the proposed methodology