Please use this identifier to cite or link to this item: http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1383
Reference fields analysis of a Markov random field model to improve image segmentation
ERIKA DANAE LOPEZ ESPINOZA
LEOPOLDO ALTAMIRANO ROBLES
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Image segmentation
Unsupervised segmentation
Markov random field
Non-homogeneous random field
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%.
Journal of Applied Research and Technology
2010
Artículo
Inglés
Estudiantes
Investigadores
Público en general
López-Espinoza, E.D. & 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
CIENCIA DE LOS ORDENADORES
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Appears in Collections:Artículos de Ciencias Computacionales

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