Título
Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation
Autor
MARIANO JOSE JUAN RIVERA MERAZ
Nivel de Acceso
Acceso Abierto
Materias
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
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Investigadores
Repositorio Orígen
Repositorio Institucional CIMAT
Descargas
362