Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2010
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| _version_ | 1876477833529786368 |
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| author | E. D. López-Espinoza |
| author_facet | E. D. López-Espinoza |
| contents | Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation E. D. López-Espinoza L. Altamirano-Robles Ingeniería non Image segmentation Markov random field homogeneous random field unsupervised 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 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%. 2010 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47415887010 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.8 |
| format | Artículo científico |
| id | redalyc_47415887010 |
| institution | Redalyc |
| language | en |
| publishDate | 2010 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation E. D. López-Espinoza Ingeniería non Image segmentation Markov random field homogeneous random field unsupervised segmentation Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation E. D. López-Espinoza L. Altamirano-Robles Ingeniería non Image segmentation Markov random field homogeneous random field unsupervised 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 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%. 2010 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47415887010 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.8 |
| title | Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation |
| topic | Ingeniería non Image segmentation Markov random field homogeneous random field unsupervised segmentation |
| url | https://www.redalyc.org/articulo.oa?id=47415887010 |