Image Asymmetry Measurement for the Study of Endangered Pygmy Bluetongue Lizard

Author version made available in accordance with the publisher's policyAbstract—There are applications for the measurement of body asymmetry as some studies have shown a correlation between asymmetry and fitness for some species. In our study of the endangered Pygmy Bluetongue Lizard, the asymmetry of its head is being investigated to see whether this has a correlation with its health and chance of survival in the wild. As there are restrictions on handling the endangered lizards, their digital photos must be taken in the field and therefore it is difficult to impose restrictions on the conditions under which the digital images are acquired. In this paper, we propose a novel automatic technique that is invariant to rotation, size, illumination and tilt, for the measurement of lizard symmetry based on its digital imagery and the resulting symmetry index is used to infer the lizard’s asymmetry. The conventional manual methods being used by biologists for fluctuating asymmetry measurement have a number of disadvantages including human errors, and their methods of measurement are based on counting the number of scales and length measurement that do not often agree well with visual assessment. Our proposed image processing technique is non-invasive, robust in a way that will give a similar symmetric index for different images of the same lizard, and more importantly based on the actual image scale pattern of the lizards. Hence our proposed method will also give a better agreement with visual assessment

Pre-processing Techniques to Improve the Efficiency of Video Identification for the Pygmy Bluetongue Lizard

Copyright 2015 SCITEPRESS (Science and Technology Publications, Lda.). Published version of the paper reproduced here with permission from the publisherIn the study of the endangered Pygmy Bluetongue Lizard, non-invasive photographic identification is preferred to the current invasive methods which can be unreliable and cruel. As the lizard is an endangered species, there are restrictions on its handling. The lizard is also in constant motion and it is therefore difficult to capture a good still image for identification purposes. Hence video capture is preferred as a number of images of the lizard at various positions and qualities can be collected in just a few seconds from which the best image can be selected for identification. With a large number of individual lizards in the database, matching a video sequence of images against each database image for identification will render the process very computationally inefficient. Moreover, a large portion of those images are non-identifiable due to motion and optical blur and different body curvature to the reference database image. In this paper, we propose a number of pre-processing techniques for pre-selecting the best image out of the video image sequence for identification. Using our proposed pre-selection techniques, it has been shown that the computational efficiency can be significantly improved