Systems Seminar

Image Segmentation Under Wavelet-Based (and Other) Priors

Prof. Mario A. T. Figueiredo
Department of Electrical and Computer Engineering
Instituto Superior Tecnico (IST)

Abstract

I will describe a new approach to Bayesian image segmentation which opens the door to the use of a class of spatial priors which, up to now, were reserved for estimation problems (such as image denoising or restoration). In particular, I will show how wavelet-based spatial priors can be used for image segmentation. The proposed formulation can be tailored to supervised, unsupervised, or semi-supervised segmentation, and with any probabilistic (parametric or not) observation model for any chosen features (intensities, multispectral, texture features, etc.).

The main obstacle to using wavelet-based priors for segmentation (or any other priors for continuous-valued images, such as Gauss Markov models) is that they are aimed at representing real values, rather than the discrete (categorical) labels needed for segmentation. I'll show how this difficulty can be sidestepped by introducing real-valued hidden fields, to which the labels are probabilistically related. A (generalized) expectation-maximization algorithm can then be derived to perform "maximum a posteriori" segmentation under this model. Experiments on synthetic and real data testify for the adequacy of the approach.

This is an extended version of a recent oral presentation at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition - CVPR'2005, San Diego, CA, June 2005.

Time and Place: Mon., Aug. 29, at 3:30 pm in 2534 Engr. Hall.       *** NOTE SPECIAL DAY & ROOM ***

SYSTEMS SEMINAR WEB PAGE: http://homepages.cae.wisc.edu/~gubner/seminar/

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