A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns

TL;DR

This paper introduces an unsupervised segmentation method combining Markov Random Fields and active contours to identify animal spot patterns, specifically applied to Diploglossus millepunctatus lizards, achieving up to 91.11% efficiency.

Contribution

It presents a novel unsupervised segmentation approach using MRFs and active contours for animal pattern recognition, reducing the need for parameter tuning in classic methods.

Findings

Achieved up to 91.11% segmentation efficiency.

Compared favorably to classic supervised segmentation methods.

Demonstrated effectiveness on Diploglossus millepunctatus lizard images.

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 $91.11\%$.