A Markov random field image segmentation model for lizard spots
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| Format: | Artículo científico |
| Language: | en |
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Universidad de Antioquia
2016
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| _version_ | 1876458332873555968 |
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| author | Alexander Gómez-Villa |
| author_facet | Alexander Gómez-Villa |
| contents | A Markov random field image segmentation model for lizard spots Alexander Gómez-Villa Germán Díez-Valencia Augusto Enrique Salazar-Jimenez Ingeniería Graph Cuts Markov network animal biometrics Belief propagation Markov random field Animal identification as a method for fauna study and conservation can be implemented using phenotypic appearance features such as spots, stripes or morphology. This procedure has the advantage that it does not harm study subjects. The visual identification of the subjects must be performed by a trained professional, who may need to inspect hundreds or thousands of images, a time-consuming task. In this work, several classical segmentation and preprocessing techniques, such as threshold, adaptive threshold, histogram equalization, and saturation correction are analyzed. Instead of the classical segmentation approach, herein we propose a Markov random field segmentation model for spots, which we test under ideal, standard and challenging acquisition conditions. As study subject, the Diploglossus millepunctatus lizard is used. The proposed method achieved a maximum efficiency of 84.87%. 2016 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43045911005 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.79 |
| format | Artículo científico |
| id | redalyc_43045911005 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Universidad de Antioquia |
| spellingShingle | A Markov random field image segmentation model for lizard spots Alexander Gómez-Villa Ingeniería Graph Cuts Markov network animal biometrics Belief propagation Markov random field A Markov random field image segmentation model for lizard spots Alexander Gómez-Villa Germán Díez-Valencia Augusto Enrique Salazar-Jimenez Ingeniería Graph Cuts Markov network animal biometrics Belief propagation Markov random field Animal identification as a method for fauna study and conservation can be implemented using phenotypic appearance features such as spots, stripes or morphology. This procedure has the advantage that it does not harm study subjects. The visual identification of the subjects must be performed by a trained professional, who may need to inspect hundreds or thousands of images, a time-consuming task. In this work, several classical segmentation and preprocessing techniques, such as threshold, adaptive threshold, histogram equalization, and saturation correction are analyzed. Instead of the classical segmentation approach, herein we propose a Markov random field segmentation model for spots, which we test under ideal, standard and challenging acquisition conditions. As study subject, the Diploglossus millepunctatus lizard is used. The proposed method achieved a maximum efficiency of 84.87%. 2016 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43045911005 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.79 |
| title | A Markov random field image segmentation model for lizard spots |
| topic | Ingeniería Graph Cuts Markov network animal biometrics Belief propagation Markov random field |
| url | https://www.redalyc.org/articulo.oa?id=43045911005 |