Bayesian Image Segmentation Using Wavelet-Based Priors

This paper introduces a formulation which allows using wavelet-based priors for image segmentation. This formulation can be used in supervised, unsupervised, or semisupervised 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-theart 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 they're aimed at representing real values, rather than discrete labels, as needed for segmentation.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Mário A. T. Figueiredo
• Keywords: Computer Vision, CVPR 2005, Image Segmentation, Piece-wise Smoothness, Probabilistic Observation Model, Wavelet-based Priors, Wavelet-based Processing