Automatic segmentation of lizard spots using an active contour model

TL;DR

This paper presents an automated segmentation algorithm for identifying spots on an endangered lizard species using active contours, combining preprocessing, morphology, and parameter optimization, achieving over 78% accuracy.

Contribution

It introduces a novel segmentation method specifically designed for Diploglossus millepunctatus, integrating optimization for parameter selection to improve accuracy.

Findings

Achieved 78.37% correct segmentation accuracy

Demonstrated the feasibility of automated spot segmentation in endangered species

Combined preprocessing, active contours, and morphology effectively

Abstract

Animal biometrics is a challenging task. In the literature, many algorithms have been used, e.g. penguin chest recognition, elephant ears recognition and leopard stripes pattern recognition, but to use technology to a large extent in this area of research, still a lot of work has to be done. One important target in animal biometrics is to automate the segmentation process, so in this paper we propose a segmentation algorithm for extracting the spots of Diploglossus millepunctatus, an endangered lizard species. The automatic segmentation is achieved with a combination of preprocessing, active contours and morphology. The parameters of each stage of the segmentation algorithm are found using an optimization procedure, which is guided by the ground truth. The results show that automatic segmentation of spots is possible. A 78.37 % of correct segmentation in average is reached.