A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns
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 \(91.11\,\%\).
KeywordsInput Image Gaussian Mixture Model Active Contour Markov Random Field Histogram Equalization
The authors express thanks to projects 512C-2013 Ruta N and P13124 from ITM for their support. Also, Fundación Malpelo and Parques Nacionales de Colombia provided funding and research permits for collecting data at the Santuario de Fauna y Flora Isla de Malpelo.
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