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EMMCVPR
1999
Springer

Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach

8 years 10 months ago
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
José M. B. Dias
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where EMMCVPR
Authors José M. B. Dias
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