Advertisement

A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns

  • Alexander GómezEmail author
  • German Díez
  • Jhony Giraldo
  • Augusto Salazar
  • Juan M. Daza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

Non-intrusive biometrics of animals using images allows to analyze phenotypic populations and individuals with patterns like stripes and spots without affecting the studied subjects. However, non-intrusive biometrics demand a well trained subject or the development of computer vision algorithms that ease the identification task. In this work, an analysis of classic segmentation approaches that require a supervised tuning of their parameters such as threshold, adaptive threshold, histogram equalization, and saturation correction is presented. In contrast, a general unsupervised algorithm using Markov Random Fields (MRF) for segmentation of spots patterns is proposed. Active contours are used to boost results using MRF output as seeds. As study subject the Diploglossus millepunctatus lizard is used. The proposed method achieved a maximum efficiency of \(91.11\,\%\).

Keywords

Input Image Gaussian Mixture Model Active Contour Markov Random Field Histogram Equalization 
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

Acknowledgment

The authors express thanks to projects 512C-2013 Ruta N and P13124 from ITM for their support. Also, Fundación Malpelo and Parques Nacionales de Colombia provided funding and research permits for collecting data at the Santuario de Fauna y Flora Isla de Malpelo.

References

  1. 1.
    Kühl, H.S., Burghardt, T.: Animal biometrics: quantifying and detecting phenotypic appearance. Trends Ecol. Evol. 28(7), 432–441 (2013)CrossRefGoogle Scholar
  2. 2.
    Kelly, M.J.: Computer-aided photograph matching in studies using individual identification: an example from serengeti cheetahs. J. Mammal. 82(2), 440–449 (2001)CrossRefGoogle Scholar
  3. 3.
    Lahiri, M., Tantipathananandh, C., Warungu, R., Rubenstein, D.I., Berger-Wolf, T.Y.: Biometric animal databases from field photographs: identification of individual zebra in the wild. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, page 6. ACM (2011)Google Scholar
  4. 4.
    Bolger, D.T., Morrison, T.A., Vance, B., Lee, D., Farid, H.: A computer-assisted system for photographic mark-recapture analysis. Methods Ecol. Evol. 3(5), 813–822 (2012)CrossRefGoogle Scholar
  5. 5.
    Gope, C., Kehtarnavaz, N., Hillman, G., Würsig, B.: An affine invariant curve matching method for photo-identification of marine mammals. Pattern Recogn. 38(1), 125–132 (2005)CrossRefGoogle Scholar
  6. 6.
    Ardovini, A., Cinque, L., Sangineto, E.: Identifying elephant photos by multi-curve matching. Pattern Recogn. 41(6), 1867–1877 (2008)zbMATHCrossRefGoogle Scholar
  7. 7.
    López-Victoria, M.: The lizards of malpelo (colombia): some topics on their ecology and threats. Caldasia 28(1), 129–134 (2006)Google Scholar
  8. 8.
    Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: Proceedings Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001, vol. 1, pp. 105–112. IEEE (2001)Google Scholar
  9. 9.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309–314 (2004)CrossRefGoogle Scholar
  10. 10.
    Prema Kumar, M., Ton, P.-H.-S., Zisserman, A.: Obj cut. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 18–25. IEEE (2005)Google Scholar
  11. 11.
    Kato, Z., Pong, T.-C.: A markov random field image segmentation model for color textured images. Image Vis. Comput. 24(10), 1103–1114 (2006)CrossRefGoogle Scholar
  12. 12.
    Delong, A., Boykov, Y.: Globally optimal segmentation of multi-region objects. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 285–292. IEEE (2009)Google Scholar
  13. 13.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT press, Cambridge (2009)Google Scholar
  14. 14.
    Lézoray, O., Grady, L.: Image processing and analysis with graphs: theory and practice. CRC Press, Boca Raton (2012)Google Scholar
  15. 15.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  16. 16.
    Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K., Eggert, D.W., Fitzgibbon, A., Fisher, R.B.: An experimental comparison of range image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 673–689 (1996)CrossRefGoogle Scholar
  17. 17.
    Holmberg, J., Norman, B., Arzoumanian, Z.: Estimating population size, structure, and residency time for whale sharks rhincodon typus through collaborative photo-identification. Endangered Species Res. 7, 39–53 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Gómez
    • 1
    Email author
  • German Díez
    • 1
  • Jhony Giraldo
    • 1
    • 2
    • 3
  • Augusto Salazar
    • 1
    • 2
  • Juan M. Daza
    • 3
  1. 1.Grupo de Investigación SISTEMIC, Facultad de IngenieríaUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Grupo de Investigación AEyCC, Facultad de IngenieríasInstituto Tecnológico Metropolitano ITMMedellínColombia
  3. 3.Grupo Herpetológico de Antioquia, Instituto de BiologíaUniversidad de AntioquiaMedellínColombia

Personalised recommendations