Repositorio Institucional UDEA
Article

A Markov random field image segmentation model for lizard spots

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 tra...

Full description

Main Author: Gómez Villa, Alexander
Other Authors: Díez Valencia, Germán, Salazar Jiménez, Augusto Enrique
Format: Article
Language: eng
Published: Universidad de Antioquia, Facultad de Ingeniería 2016
Subjects:
Online Access: http://hdl.handle.net/10495/5974
Summary:
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%.