A Markov random field image segmentation model for lizard spots
Animal identification as a method for fauna study and conservation can be implemented using phenotypic appearance features such as spots, stripes or morphology. This procedure has the advantage that it does not harm study subjects. The visual identification of the subjects must be performed by a trained professional, who may need to inspect hundreds or thousands of images, a time-consuming task. In this work, several classical segmentation and preprocessing techniques, such as threshold, adaptive threshold, histogram equalization, and saturation correction are analyzed. Instead of the classical segmentation approach, herein we propose a Markov random field segmentation model for spots, which we test under ideal, standard and challenging acquisition conditions. As study subject, the Diploglossus millepunctatus lizard is used. The proposed method achieved a maximum efficiency of 84.87%.
L. Hiby et al., “A tiger cannot change its stripes: using a three-dimensional model to match images of living tigers and tiger skins”, Biology Letters, vol. 5, no. 3, pp. 383-386, 2009.
H. Kühl and T. Burghardt, “Animal biometrics: quantifying and detecting phenotypic appearance”, Trends in Ecology & Evolution, vol. 28, no. 7, pp. 432-441, 2013.
M. Kelly, “Computer-aided photograph matching in studies using individual identification: an example from Serengeti cheetahs”, Journal of Mammalogy, vol. 82, no. 2, pp. 440-449, 2001.
M. Lahiri, C. Tantipathananandh, R. Warungu, D. Rubenstein and T. Berger, “Biometric animal databases from field photographs: identification of individual zebra in the wild”, in 1st ACM International Conference on Multimedia Retrieval (ICMR) Trento, Italy, 2011.
D. Bolger, T. Morrison, B. Vance, D. Lee and H. Farid, “A computer-assisted system for photographic mark–recapture analysis”, Methods in Ecology and Evolution, vol. 3, no. 5, pp. 813-822, 2012.
A. Ardovini, L. Cinque and E. Sangineto, “Identifying elephant photos by multi-curve matching”, Pattern Recognition, vol. 41, no. 6, pp. 1867-1877, 2008.
B. Araabi, N. Kehtarnavaz, T. McKinney, G. Hillman and B. Würsig, “A string matching computer-assisted system for dolphin photo-identification”, Annals of Biomedical Engineering, vol. 28, no. 10, pp. 1269-1279, 2000.
C. Gope, N. Kehtarnavaz, G. Hillman and B. Würsig, “An affine invariant curve matching method for photo-identification of marine mammals”, Pattern Recognition, vol. 38, no. 1, pp. 125-132, 2005.
E. Ranguelova and E. Pauwels, “Saliency detection and matching strategy for photo-identification of humpback whales”, in ICGST International conference on Graphics, Vision and Image Processing (GVIP), Cairo, Egypt, 2005, pp. 81-88.
E. Ranguelova, M. Huiskes and E. Pauwels, “Towards computer-assisted photo-identification of humpback whales”, in International Conference on Image Processing (ICIP), Singapore, Singapore, 2004, pp. 1727-1730.
P. Zeeuw, E. Pauwels, E. Ranguelova, D. Buonantony and S. Eckert, “Computer assisted photo identification of Dermochelys coriacea”, in Int. Conference on Pattern Recognition (ICPR), Berlin, Germany, 2010, pp. 165-172.
L. Hiby and P. Lovell, “Computer aided matching of natural markings: a prototype system for grey seals”, International Whaling Commission, Noordwijkerhout, Netherlands, Report (Special Issue 12), 1990.
J. Duyck et al., “Sloop: A pattern retrieval engine for individual animal identification”, Pattern Recognition, vol. 48, no. 4, pp. 1059-1073, 2015.
M. López, “The lizards of Malpelo (Colombia): some topics on their ecology and threats”, Caldasia, vol. 28, no. 1, pp. 129-134, 2006.
Y. Boykov and M. Jolly, “Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images” in 8th IEEE International Conference on Computer Vision (ICCV), Vancouver, Canada, 2001, pp. 105-112.
C. Rother, V. Kolmogorov and A. Blake, “‘GrabCut’: Interactive foreground extraction using iterated graph cuts”, ACM Transactions on Graphics, vol. 23, no. 3, pp. 309-314, 2004.
M. Kumar, P. Torr and A. Zisserman, “Obj cut”, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, USA, 2005, pp. 18-25.
Z. Kato and T. Pong, “A Markov random field image segmentation model for color textured images”, Image and Vision Computing, vol. 24, no. 10, pp. 1103-1114, 2006.
A. Delong and Y. Boykov, “Globally optimal segmentation of multi-region objects”, in 12th International Conference on Computer Vision, Kyoto, Japan, 2009, pp. 285-292.
D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, 1st ed. Cambridge, USA: MIT Press, 2009.
A. Goldberg, S. Hed, H. Kaplan, R. Tarjan and R. Werneck, “Maximum flows by incremental breadth-first search”, in 19th Annual European Symposium, Saarbrücken, Germany, 2011, pp. 457-468.
O. Lézoray and L. Grady, Image processing and analysis with graphs: theory and practice, 1st ed. San Jose, USA: CRC Press, 2012.
A. Ihler, J. Fisher and A. Willsky, “Loopy belief propagation: Convergence and effects of message errors”, Journal of Machine Learning Research, vol. 6, pp. 905-936, 2005.
N. Komodakis, N. Paragios and G. Tziritas, “MRF optimization via dual decomposition: Message-passing revisited”, in 11th International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, 2007, pp. 1-8.
B. Andres, T. Beier and J. Kappes, OpenGM: A C++ Library for Discrete Graphical Models, 2012. [Online]. Available: http://arxiv.org/abs/1206.0111. Accessed on: Mar. 29, 2016.
J. Holmberg, B. Norman and Z. Arzoumanian, “Estimating population size, structure, and residency time for whale sharks Rhincodon typus through collaborative photo-identification”, Endangered Species Res., vol. 7, no. 1, pp. 39-53, 2009.
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