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Segmentation of Saimaa Ringed Seals for Identification Purposes

  • Artem Zhelezniakov
  • Tuomas EerolaEmail author
  • Meeri Koivuniemi
  • Miina Auttila
  • Riikka Levänen
  • Marja Niemi
  • Mervi Kunnasranta
  • Heikki Kälviäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

Wildlife photo-identification is a commonly used technique to identify and track individuals of wild animal populations over time. It has various applications in behavior and population demography studies. Nowadays, mostly due to large and labor-intensive image data sets, automated photo-identification is an emerging research topic. In this paper, the first steps towards automatic individual identification of the critically endangered Saimaa ringed seal (Phoca hispida saimensis) are taken. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual making the image-based identification possible. We propose a superpixel classification based method for the segmentation of ringed seal in images to eliminate the background and to simplify the identification. The proposed segmentation method is shown to achieve a high segmentation accuracy with challenging image data. Furthermore, we show that using the obtained segmented images promising identification results can be obtained even with a simple texture feature based approach. The proposed method uses general texture classification techniques and can be applied also to other animal species with a unique fur or skin pattern.

Keywords

Texture Feature Support Vector Machine Classifier Scale Invariant Feature Transform Ringed Seal Camera Trap 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank the Wildlife Photo-ID Network funded by the Finnish Cultural Foundation.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Artem Zhelezniakov
    • 1
    • 3
  • Tuomas Eerola
    • 1
    Email author
  • Meeri Koivuniemi
    • 2
  • Miina Auttila
    • 2
    • 4
  • Riikka Levänen
    • 2
  • Marja Niemi
    • 2
  • Mervi Kunnasranta
    • 2
  • Heikki Kälviäinen
    • 1
    • 5
  1. 1.Machine Vision and Pattern Recognition Laboratory, School of Engineering ScienceLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.Department of BiologyUniversity of Eastern FinlandJoensuuFinland
  3. 3.Department of Computer Technologies and Control SystemsITMO UniversitySaint PetersburgRussia
  4. 4.Parks and Wildlife FinlandMetsähallitusSavonlinnaFinland
  5. 5.School of Information TechnologyMonash University MalaysiaBandar SunwayMalaysia

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