Título
Reference fields analysis of a Markov random field model to improve image segmentation
Autor
ERIKA DANAE LOPEZ ESPINOZA
LEOPOLDO ALTAMIRANO ROBLES
Nivel de Acceso
Acceso Abierto
Materias
Image segmentation - (IMAGE SEGMENTATION) Unsupervised segmentation - (UNSUPERVISED SEGMENTATION) Markov random field - (MARKOV RANDOM FIELD) Non-homogeneous random field - (NON-HOMOGENEOUS RANDOM FIELD) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
In Markov random field (MRF) models, parameters such as internal and external reference fields are used. In this paper, the influence of these parameters in the segmentation quality is analyzed, and it is shown that, for image segmentation, a MRF model with a priori energy function defined by means of non-homogeneous internal and external field has better segmentation quality than a MRF model defined only by a homogeneous internal reference field. An analysis of the MRF models in terms of segmentation quality, computational time and tests of statistical significance is done. Significance tests showed that the segmentations obtained with MRF model defined by means of non-homogeneous reference fields are significant at levels of 85% and 75%.
Editor
Journal of Applied Research and Technology
Fecha de publicación
2010
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Relación
&
Altamirano-Robles, L. (2010). Reference fields analysis of a Markov random field model to improve image segmentation, Journal of Applied Research and Technology, Vol. 8 (2): 260-273
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
López-Espinoza, E.D.
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
315