Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation
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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | México |
| Institución: | Universidad Nacional Autónoma de México |
| Repositorio: | Redalyc-UNAM |
| OAI Identifier: | oai:redalyc.org:47415887010 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=47415887010 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería non Image segmentation Markov random field homogeneous random field unsupervised segmentation |
| Sumario: | 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%. |
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