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Journal of applied research and technology

versión impresa ISSN 1665-6423

J. appl. res. technol vol.8 no.2 México ago. 2010

 

Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation

 

E. D. López–Espinoza*1, L. Altamirano–Robles2

 

1 Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Del. Coyoacan, México, D.F., CP 04510. *E–mail: danae@atmosfera.unam.mx

2 Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, Sta. Ma. Tonantzintla Puebla, México, CP 72840.

 

ABSTRACT

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%.

Keywords: Image segmentation, unsupervised segmentation, Markov random field, non–homogeneous random field.

 

RESUMEN

En modelos de Campos Aleatorios de Markov (MRF) se emplean parámetros como el campo de referencia interno y externo. En este artículo, analizamos su influencia en la calidad de la segmentación final, y mostramos que, para segmentación de imágenes, un modelo MRF con una función de energía definida mediante un campo de referencia interno y uno externo no homogéneos, obtiene mejores calidades de segmentación que un modelo MRF definido a través de un solo campo de referencia interno homogéneo. El análisis de los modelos MRF es realizado en términos de la calidad de segmentación, tiempo computacional y pruebas de significancia estadística. Las pruebas de significancia mostraron que los resultados de segmentación obtenidos con el modelo MRF definido a través de campos de referencia no homogéneos son significativos en un nivel del 85% y 75%.

 

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