access icon openaccess Automatic individual identification of Saimaa ringed seals

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 28/02/2017, Accepted 17/10/2017, Revised 22/09/2017, Published 24/10/2017

In order to monitor an animal population and to track individual animals in a non-invasive way, identification of individual animals based on certain distinctive characteristics is necessary. In this study, automatic image-based individual identification of the endangered Saimaa ringed seal (Phoca hispida saimensis) is considered. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual. This can be used as a basis for the identification process. The authors propose a framework that starts with segmentation of the seal from the background and proceeds to various post-processing steps to make the pelage pattern more visible and the identification easier. Finally, two existing species independent individual identification methods are compared with a challenging data set of Saimaa ringed seal images. The results show that the segmentation and proposed post-processing steps increase the identification performance.

Inspec keywords: image segmentation

Other keywords: distinctive permanent pelage pattern; seal segmentation; animal population monitoring; automatic image-based individual identification; Phoca hispida saimensis; endangered Saimaa ringed seal

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

References

    1. 1)
      • 18. Cheng, J., Liu, J., Xu, Y., et al: ‘Superpixel classification based optic disc and optic cup segmentation for glaucoma screening’, IEEE Trans. Med. Imaging, 2013, 32, (6), pp. 1019–1032.
    2. 2)
      • 8. Halloran, K.M., Murdoch, J.D, Becker, M.S.: ‘Applying computer aided photo-identification to messy datasets: a case study of Thornicroft's giraffe (Giraffa camelopardalis thornicrofti)’, Afr. J. Ecol., 2014, 53, (2), pp. 147–155.
    3. 3)
      • 22. Arbeláez, P., Pont-Tuset, J., Barron, J., et al: ‘Multiscale combinatorial grouping’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 328–335.
    4. 4)
      • 17. Yu, X., Wang, J., Kays, R., et al: ‘Automated identification of animal species in camera trap images’, EURASIP J. Image Video Process., 2013, 2013, (1), p. 52.
    5. 5)
      • 13. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91–110.
    6. 6)
      • 14. Kniest, E., Burns, D., Harrison, P.: ‘Fluke matcher: a computer-aided matching system for humpback whale (Megaptera novaeangliae) flukes’, Mar. Mammal Sci., 2010, 26, (3), pp. 744–756.
    7. 7)
      • 3. Koivuniemi, M., Auttila, M., Niemi, M., et al: ‘Photo-id as a tool for studying and monitoring the endangered Saimaa ringed seal’, Endangered Species Res., 2016, 30, pp. 29–36.
    8. 8)
      • 6. Tharwat, A., Gaber, T., Hassanien, A., et al: ‘Cattle identification using muzzle print images based on texture features approach’. Proc. of the Fifth Int. Conf. on Innovations in Bio-Inspired Computing and Applications, 2014, pp. 217–227.
    9. 9)
      • 24. Ahonen, T., Matas, J., He, C., et al: ‘Rotation invariant image description with local binary pattern histogram Fourier features’. 16th Scandinavian Conf. on Image Analysis, 2009, pp. 61–70.
    10. 10)
      • 2. Auttila, M., Niemi, M., Skrzypczak, T., et al: ‘Estimating and mitigating perinatal mortality in the endangered Saimaa ringed seal (phoca hispida saimensis) in a changing climate’, Ann. Zoologici Fennici, 2014, 51, (6), pp. 526–534.
    11. 11)
      • 26. Pizer, S.M., Amburn, E.P, Austin, J.D., et al: ‘Adaptive histogram equalization and its variations’, Comput. Vis. Graph. Image Process., 1987, 39, (3), pp. 355–368.
    12. 12)
      • 10. Branzan Albu, A., Wiebe, G., Govindarajulu, P., et al: ‘Towards automatic model based identification of individual sharp-tailed snakes from natural body markings’. In Proc. of ICPR Workshop on Animal and Insect Behaviour, Tampa, FL, USA, 2008.
    13. 13)
      • 20. Arbelaez, P., Maire, M., Fowlkes, C, et al: ‘From contours to regions: an empirical evaluation’. IEEE Conf. on Computer Vision and Pattern Recognition, June 2009, pp. 2294–2301.
    14. 14)
      • 7. Hoque, S, Hannan Bin Azhar, M.A., Deravi, F.: ‘ZOOMETRICS-biometric identification of wildlife using natural body marks’, Int. J. Bio-Science Bio-Technology, 2011, 3, (3), pp. 45–53.
    15. 15)
      • 4. Zhelezniakov, A., Eerola, T., Koivuniemi, M., et al: ‘Segmentation of Saimaa ringed seals for identification purposes’. Proc. of Int. Symp. on Advances in Visual Computing (ISVC), Las Vegas, USA, 2015, (LNCS, 9475), pp. 227–236.
    16. 16)
      • 12. Adams, J.D, Speakman, T., Zolman, E., et al: ‘Automating image matching, cataloging, and analysis for photo-identification research’, Aquatic Mammals, 2006, 32, (3), p. 374.
    17. 17)
      • 1. Kovacs, K.M., Aguilar, A, Aurioles, D., et al: ‘Global threats to pinnipeds’, Mar. Mammal Sci., 2012, 28, (2), pp. 414–436.
    18. 18)
      • 27. Fischler, M.A., Bolles, R.C.: ‘Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography’ (Communications of the Association for Computing Machinery (ACM), 1981), pp. 381–395.
    19. 19)
      • 21. Martin, D., Fowlkes, C., Tal, D., et al: ‘A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics’. Proc. of the 8th Int. Conf. on Computer Vision, July 2001, vol. 2, pp. 416–423.
    20. 20)
      • 25. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on featured distributions’, Pattern Recognit., 1996, 29, (1), pp. 51–59.
    21. 21)
      • 23. Ojansivu, V., Heikkilä, J.: ‘Blur insensitive texture classification using local phase quantization’. 3rd Int. Conf. on Image and Signal Processing, 2008, pp. 236–243.
    22. 22)
      • 11. Kayci, L., Kaya, Y., Tekin, R.: ‘A computer vision system for the automatic identification of butterfly species via Gabor-filter-based texture features and extreme learning machine: GF + ELM’, TEM J., 2013, 2, (1), pp. 13–20.
    23. 23)
      • 29. Jonathon Phillips, P., Moon, H, Rizvi, S.A, et al: ‘The FERET evaluation methodology for face recognition algorithms’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (10), pp. 1090–1104.
    24. 24)
      • 19. Arbelaez, P., Maire, M., Fowlkes, C., et al: ‘Contour detection and hierarchical image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (5), pp. 898–916.
    25. 25)
      • 9. Bendik, N.F., Morrison, T.A., Gluesenkamp, A.G., et al: ‘Computer-assisted photo identification outperforms visible implant elastomers in an endangered salamander, Eurycea Tonkawae’, PLoS One, 2013, 8, (3), p. e59424.
    26. 26)
      • 28. McCann, S., Lowe, D.G.: ‘Local naive Bayes nearest neighbor for image classification’. Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3650–3656.
    27. 27)
      • 16. Crall, J.P., Stewart, C.V., Berger-Wolf, T.Y., et al: ‘Hotspotter - patterned species instance recognition’. Workshop on Applications of Computer Vision (WACV), January 2013, pp. 230–237.
    28. 28)
      • 15. Ranguelova, E., Pauwels, E.: ‘Saliency detection and matching strategy for photoidentification of humpback whales’. Proc. of the 2005 ICGST Int. Conf. on Graphics, Vision and Image Processing, 2005, pp. 81–88.
    29. 29)
      • 5. Anderson, C.: ‘Individual identification of polar bears by whisker spot patterns’. PhD thesis, University of Central Florida, Orlando, Florida, 2007.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0082
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0082
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading