Título

Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation

Autor

MARIANO JOSE JUAN RIVERA MERAZ

Nivel de Acceso

Acceso Abierto

Resumen o descripción

We present a new Markov random field (MRF) based

model for parametric image segmentation. Instead of directly

computing a label map, our method computes the probability that

the observed data at each pixel is generated by a particular intensity

model. Prior information about segmentation smoothness

and low entropy of the probability distribution maps is codified in

the form of a MRF with quadratic potentials so that the optimal

estimator is obtained by solving a quadratic cost function with

linear constraints. Although, for segmentation purposes, the mode

of the probability distribution at each pixel is naturally used as an

optimal estimator, our method permits the use of other estimators,

such as the mean or the median, which may be more appropriate

for certain applications. Numerical experiments and comparisons

with other published schemes are performed, using both synthetic

images and real data of brain MRI for which expert hand-made

segmentations are available. Finally, we show that the proposed

methodology may be easily extended to other problems, such as

stereo disparity estimation.

Editor

IEEE

Fecha de publicación

2007

Tipo de publicación

Artículo

Versión de la publicación

Versión publicada

Formato

application/pdf

Idioma

Inglés

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional CIMAT

Descargas

362

Comentarios



Necesitas iniciar sesión o registrarte para comentar.