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Automated salamander recognition using deep neural networks and feature extraction

Bakløkken, Jørgen; Schoeler, Felix; Nørholm, Hugo; George, Sony; Pedersen, Marius; Dervo, Børre Kind
Journal article, Peer reviewed
Published version
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Bakløkken (6.694Mb)
URI
http://hdl.handle.net/11250/2639554
Date
2019
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  • Institutt for datateknologi og informatikk [3870]
  • Publikasjoner fra CRIStin - NTNU [20843]
Original version
NIK: Norsk Informatikkonferanse. 2019, .  
Abstract
This paper presents a study conducted to recognize salamanders by using their unique body markings based on images. The detection and matching of unique patterns in a salamander’s body can be complex due variability in individual animals size, shape, orientation and also influence from the external enviornment. While traditional methods require time intensive manual image corrections of the salamanders to achieve accurate recognition, in this work we propose a fully automatic techinque for straigthening. We also propose a matching technique based on the corrected images. The convolutional neural network ResNet50 and dense scale-invariant feature transform (DSIFT) are used for belly pattern localization, and matching for salamander recognition.
Publisher
Bibsys Open Journal Systems
Journal
NIK: Norsk Informatikkonferanse

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