A Markov random field image segmentation model for lizard spots

  • Alexander Gomez-Villa Universidad de Antioquia https://orcid.org/0000-0003-0469-3425
  • German Diez-Valencia Universidad de Antioquia
  • Augusto Enrique Salazar-Jimenez Instituto Tecnológico Metropolitano
Keywords: 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 Gomez-Villa, Universidad de Antioquia
Embedded Systems and Computational Intelligence Group (SISTEMIC), Faculty of Engineering
German Diez-Valencia, Universidad de Antioquia
Embedded Systems and Computational Intelligence Group (SISTEMIC), Faculty of Engineering
Augusto Enrique Salazar-Jimenez, Instituto Tecnológico Metropolitano
Automation, Electronics and Computational Sciences Group (AEyCC), Faculty of Engineering


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How to Cite
Gomez-Villa A., Diez-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