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Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms
Nov 09, 2016
Animal biometrics is an important requirement for monitoring and conservation tasks. The classical animal biometrics risk the animals' integrity, are expensive for numerous animals, and depend on expert criterion. The non-invasive biometrics techniques offer alternatives to manage the aforementioned problems. In this paper we propose an automatic segmentation and identification algorithm based on artificial vision algorithms to recognize Diploglossus millepunctatus. Diploglossus millepunctatus is an endangered lizard species. The algorithm is based on two stages: automatic segmentation to remove the subjective evaluation, and one identification stage to reduce the analysis time. A 82.87% of correct segmentation in average is reached. Meanwhile the identification algorithm is achieved with euclidean distance point algorithms such as Iterative Closest Point and Procrustes Analysis. A performance of 92.99% on the top 1, and a 96.82% on the top 5 is reached. The developed software, and the database used in this paper are publicly available for download from the web page of the project.
A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns
Oct 26, 2015
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 $91.11\%$.