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Automatic Picture-Matching of Crested Newts

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Cooperative Design, Visualization, and Engineering (CDVE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12983))

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Abstract

This article highlights a group of image processing, deep learning, and pattern matching techniques that can be used together in order to automatically identify different specimens of newts from a single species (Triturus cristatus Laurenti 1768). First, each image of newt will be: augmented, segmented, straightened. Then, patterns of images will be detected and compared between each other, allowing the differentiation of newts living in selected areas.

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Notes

  1. 1.

    http://www.amphident.de/en/index.html.

  2. 2.

    https://scikit-image.org/.

  3. 3.

    http://www.robots.ox.ac.uk/~vgg/software/via/via.html.

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Acknowledgement

We would like to thanks Christian Hundt from NVIDIA AI Technology Center, for his very valuable advices throughout the creation of this work. Thanks to Remy Haas and Lionel L’Hoste for retrieving the pictures on the field and annotating them in Newtrap Manager. This work has been financed by the Luxembourg FNR through the POC17 NEWTRAP.

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Authors and Affiliations

  1. Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, 4422, Belvaux, Luxembourg

    Guillaume Magnette, Yoanne Didry & Xavier Mestdagh

Authors
  1. Guillaume Magnette
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  2. Yoanne Didry
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  3. Xavier Mestdagh
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Corresponding authors

Correspondence to Yoanne Didry or Xavier Mestdagh .

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Editors and Affiliations

  1. University of the Balearic Islands, Palma, Mallorca, Spain

    Yuhua Luo

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Magnette, G., Didry, Y., Mestdagh, X. (2021). Automatic Picture-Matching of Crested Newts. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2021. Lecture Notes in Computer Science(), vol 12983. Springer, Cham. https://doi.org/10.1007/978-3-030-88207-5_32

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  • DOI: https://doi.org/10.1007/978-3-030-88207-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88206-8

  • Online ISBN: 978-3-030-88207-5

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