Texture map: an effective representation for image segmentation
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Bayesian image segmentation with mean shift
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bayesian image segmentation using gaussian field priors
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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This paper introduces a formulation which allows using wavelet-based priors for image segmentation. This formulation can be used in supervised, unsupervised, or semi-supervised modes, and with any probabilistic observation model (intensity, multispectral, texture). Our main goal is to exploit the well-known ability of wavelet-based priors to model piece-wise smoothness (which underlies state-of-the-art methods for denoising, coding, and restoration) and the availability of fast algorithms for wavelet-based processing. The main obstacle to using wavelet-based priors for segmentation is that theyýre aimed at representing real values, rather than discrete labels, as needed for segmentation. This difficulty is sidestepped by the introduction of real-valued hidden fields, to which the labels are probabilistically related. These hidden fields, being unconstrained and real-valued, can be given any type of spatial prior, such as one based on wavelets. Under this model, Bayesian MAP segmentation is carried out by a (generalized) EM algorithm. Experiments on synthetic and real data testify for the adequacy of the approach.