Authors:
Damian Tohl
;
Jimmy Li
and
C. Michael Bull
Affiliation:
Flinders University, Australia
Keyword(s):
Video Identification, Pygmy Bluetongue Lizard, Curvature, DWT, SIFT.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Shape Representation and Matching
Abstract:
In 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-pr
ocessing 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.
(More)