A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns
This addresses the need for automated identification in animal biometrics, but it is incremental as it builds on classic segmentation methods with a hybrid approach.
The paper tackled the problem of segmenting animal spot patterns for non-intrusive biometrics by proposing an unsupervised algorithm combining Markov Random Fields and active contours, achieving a maximum efficiency of 91.11% on lizard images.
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 $91.11\%$.