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Automated Visual Fin Identification of Individual Great White Sharks

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)542–557
Number of pages16
JournalInternational Journal of Computer Vision
Volume122
Journal issue3
Early online date13 Oct 2016
DOIs
StatePublished - May 2017

Abstract

This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics. The approach put forward appreciates shark fins as textureless, flexible and partially occluded objects with an individually characteristic shape. In order to recover animal identities from an image we first introduce an open contour stroke model, which extends multi-scale region segmentation to achieve robust fin detection. Secondly, we show that combinatorial, scale-space selective fingerprinting can successfully encode fin individuality. We then measure the species-specific distribution of visual individuality along the fin contour via an embedding into a global ‘fin space’. Exploiting this domain, we finally propose a non-linear model for individual animal recognition and combine all approaches into a fine-grained multi-instance framework. We provide a system evaluation, compare results to prior work, and report performance and properties in detail.

Research areas

  • Animal biometrics, Textureless object recognition, Shape analysis

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Springer Verlag at DOI: 10.1007/s11263-016-0961-y. Please refer to any applicable terms of use of the publisher.

    Final published version, 3 MB, PDF-document

    License: CC BY

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