Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach

Abstract. We propose a vector representation approach to contour 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 L2 (T) generated by a given 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

• Type: Conference
• Year: 1999
• Where: EMMCVPR
• Authors: José M. B. Dias
• Keywords: Computer Vision, EMMCVPR 1999, Estimation, Homogeneous Regions, Homogeneous Regions Contours