@inbook{c6e01e7494674b61bf04c68ed1698b33,
title = "A markov random field and active contour image segmentation model for animal spots patterns",
abstract = "Non-intrusive biometrics of animals using images allows to analyze phenotypic populations and individuals with patterns like stripes and spots without affecting the studied subjects. However, non-intrusive biometrics demand a well trained subject or the development of computer vision algorithms that ease the identification task. In this work, an analysis of classic segmentation approaches that require a supervised tuning of their parameters such as threshold, adaptive threshold, histogram equalization, and saturation correction is presented. In contrast, a general unsupervised algorithm using Markov Random Fields (MRF) for segmentation of spots patterns is proposed. Active contours are used to boost results using MRF output as seeds. As study subject the Diploglossus millepunctatus lizard is used. The proposed method achieved a maximum efficiency of 91.11\%.",
author = "Alexander Gomez-Villa and German D{\'i}ez and Jhony Giraldo and Augusto Salazar and Daza, \{Juan M.\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.",
year = "2015",
month = dec,
day = "18",
doi = "10.1007/978-3-319-27863-6\_16",
language = "English",
isbn = "9783319278629",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "173--184",
editor = "Bahram Parvin and Darko Koracin and Rogerio Feris and Gunther Weber and Ioannis Pavlidis and Tim McGraw and Regis Kopper and Zhao Ye and Eric Ragan and George Bebis and Mark Elendt and Richard Boyle",
booktitle = "Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings",
}