Markov random measure fields for image analysis Conference Paper

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Overview

abstract

• A new Bayesian formulation for the image segmentation problem is presented. It is based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators by the minimization of a differentiable function. Comparisons with existing methods on synthetic images are presented, as well as realistic applications to the segmentation of Magnetic Resonance volumes, to motion segmentation, and to edge-preserving filtering.

authors

• Arce Santana Edgar Román
• Botello S.
• Marroquin J.L.

publication date

• 2002-01-01

published in

• IEEE International Conference on Image Processing  Proceedings

Research

keywords

• Functions; Image segmentation; Magnetic resonance imaging; Markov processes; Mathematical models; Probability; Signal filtering and prediction; Bayesian formulation; Doubly stochastic prior model; Markov random measure fields; Image analysis

Additional Document Info

start page

• I/765

end page

• I/768

volume

• 1