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Automatic identification of wildlife using local binary patterns

Azhar, M.A. Hannan Bin and Hoque, Sanaul and Deravi, Farzin (2012) Automatic identification of wildlife using local binary patterns. In: IET Conference on Image Processing (IPR 2012). IET, B5-B5. ISBN 978-1-84919-632-1. (doi:10.1049/cp.2012.0454) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1049/cp.2012.0454

Abstract

Recognition of individuals is necessary for accurate estimation of wildlife population dynamics for effective management and conservation. Identifying individual wildlife by their distinctive body marks is one of the least invasive methods available. Although widely practiced, this method is mostly manual where newly captured images are compared with those in the library of previously captured images. The ability to do so automatically using computer vision techniques can improve speed and accuracy, facilitate on-field matching, and so on. This paper reports the results of using a texture based image feature descriptor, the Local Binary Patterns (LBP), for the automatic identification of an important endangered species — The Great Crested Newt (GCN). The proposed approach is tested on a database of newts' distinctive belly images which are treated as a source of biometric information. Results indicate that when both appearance and spatial information of newt belly patterns are encoded into a composite LBP feature vector, the discriminating power of the system can improve significantly.

Item Type: Book section
DOI/Identification number: 10.1049/cp.2012.0454
Uncontrolled keywords: biometrics; LBP; zoometrics; segmentation; newt; feature fusion; texture
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: P.S.P. Yapp
Date Deposited: 29 Oct 2013 16:53 UTC
Last Modified: 10 Oct 2019 10:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35799 (The current URI for this page, for reference purposes)
Hoque, Sanaul: https://orcid.org/0000-0001-8627-3429
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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