A Markov random field image segmentation model for lizard spots


  • Alexander Gómez-Villa University of Antioquia https://orcid.org/0000-0003-0469-3425
  • Germán Díez-Valencia University of Antioquia
  • Augusto Enrique Salazar-Jimenez Metropolitan Technological Institute




belief propagation, Markov network, Graph Cuts, animal biometrics, Markov random field, Diploglossus millepunctatus


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%.

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Author Biographies

Alexander Gómez-Villa, University of Antioquia

Embedded Systems and Computational Intelligence Group (SISTEMIC), Faculty of Engineering.

Germán Díez-Valencia, University of Antioquia

Embedded Systems and Computational Intelligence Group (SISTEMIC), Faculty of Engineering.

Augusto Enrique Salazar-Jimenez, Metropolitan Technological Institute

Automation, Electronics and Computational Sciences Group (AEyCC), Faculty of Engineering.


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How to Cite

Gómez-Villa, A., Díez-Valencia, G., & Salazar-Jimenez, A. E. (2016). A Markov random field image segmentation model for lizard spots. Revista Facultad De Ingeniería Universidad De Antioquia, (79), 41–49. https://doi.org/10.17533/udea.redin.n79a05

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