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TitleWhere to look when identifying roadkilled amphibians?
Author(s)Franch, Marc
Silva, Cristiano
Lopes, Gil
Ribeiro, António Fernando
Seco, Luis
Sillero, Neftalí
computer vision
survey methodology
Issue date2015
PublisherFirenze University Press
JournalActa Herpetologica
Abstract(s)Roads have multiple effects on wildlife; amphibians are one of the groups more intensely affected by roadkills. Monitoring roadkills is expensive and time consuming. Automated mapping systems for detecting roadkills, based on robotic computer vision techniques, are largely necessary. Amphibians can be recognised by a set of features as shape, size, colouration, habitat and location. This species identification by using multiple features at the same time is known as “jizz”. In a similar way to human vision, computer vision algorithms must incorporate a prioritisation process when analysing the objects in an image. Our main goal here was to give a numerical priority sequence of particular characteristics of roadkilled amphibians to improve the computing and learning process of algorithms. We asked hundred and five amateur and professional herpetologists to answer a simple test of five sets with ten images each of roadkilled amphibians, in order to determine which body parts or characteristics (body form, colour, and other patterns) are used to identify correctly the species. Anura was the group most easily identified when it was roadkilled and Caudata was the most difficult. The lower the taxonomic level of amphibian, the higher the difficulty of identifying them, both in Anura and Caudata. Roadkilled amphibians in general and Anura group were mostly identified by the Form, by the combination of Form and Colour, and finally by Colour. Caudata was identified mainly on Form and Colour and on Colour. Computer vision algorithms must incorporate these combinations of features, avoiding to work exclusively in one specific feature.
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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