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2013 | Vol. 3, No. 2 | 133--141
Tytuł artykułu

Segmentation and edge detection based on modified ant colony optimization for iris image processing

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Ant colony optimization (stocktickerACO) is a meta-heuristic algorithm inspired by food searching behavior of real ants. Recently stocktickerACO has been widely used in digital image processing. When artificial ants move in a discrete habitat like an image, they deposit pheromone in their prior position. Simultaneously, vaporizing of pheromone in each iteration step avoids from falling in the local minima trap. Iris recognition because of its great dependability and non-invasion has various applications. simulation results demonstrate stocktickerACO algorithm can effectively extract the iris texture. Also it is not sensitive to nuisance factors. Moreover, stocktickerACO in this research preserves details of the various synthetic and real images. Performance of ACO in iris segmentation is compared with operation of traditional approaches such as canny, robert, and sobel edge detections. Experimental results reveal high quality and quite promising of stocktickerACO to segment images with irregular and complex structures.

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Bibliogr. 20 poz., rys.
  • M.Sc. Student, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran,
  • Assistant professor, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran,
  • [1] P. Khaw, ”Iris recognition technology for improved authentication,” SANS Institute, 2002.
  • [2] L. Masek, ”Recognition of human iris patterns for biometric identification,” M. Thesis, The University of Western Australia, 2003.
  • [3] R. Bremananth and A. Chitra, ”New methodology for a person identification system,” Sadhana, vol. 31, pp. 259-276, 2006.
  • [4] S. Shah and A. Ross, ”Iris segmentation using geodesic active contours,” Information Forensics and Security, IEEE Transactions on, vol. 4, pp. 824-836, 2009.
  • [5] X. Liu, K. W. Bowyer, and P. J. Flynn, ”Experiments with an improved iris segmentation algorithm, 2005, pp. 118-123.
  • [6] N. Tripathy and U. Pal, ”Handwriting segmentation of unconstrained Oriya text,” Sadhana, vol. 31, pp. 755-769, 2006.
  • [7] D. Cockburn, ”A study of the validity of iris diagnosis,” The Australian Journal of Optometry, vol. 64, pp. 154-157, 1981.
  • [8] L. Ma, K. Wang, and D. Zhang, ”A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing,” Computers & Mathematics with Applications, vol. 57, pp. 1862-1868, 2009.
  • [9] M. A. Balafar, A. R. Ramli, M. I. Saripan, and S. Mashohor, ”Review of brain MRI image segmentation methods,” Artificial Intelligence Review, vol. 33, pp. 261-274, 2010.
  • [10] R. Kasturi, L. O’gorman, and V. Govindaraju, ”Document image analysis: A primer,” Sadhana, vol. 27, pp. 3-22, 2002.
  • [11] W. K. Kong and D. Zhang, ”Detecting eyelash and reflection for accurate iris segmentation,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, pp. 1025-1034, 2003.
  • [12] M. J. Aligholizadeh, S. Javadi, R. Sabbaghi-Nadooshan, and K. Kangarloo, ”An Effective Method for Eyelashes Segmentation Using Wavelet Transform,” 2011, pp. 185-188.
  • [13] Y. Chen, S. Dass, and A. Jain, ”Localized iris image quality using 2-D wavelets,” Advances in Biometrics, pp. 373-381, 2005.
  • [14] M. Mahlouji, A. Noruzi, and I. Kashan, ”Human Iris Segmentation for Iris Recognition in Unconstrained Environments,” 2012.
  • [15] V. Ramos and F. Almeida, ”Artificial ant colonies in digital image habitats-a mass behaviour effect study on pattern recognition,” Arxiv preprint cs/0412086, 2004.
  • [16] D. R. Chialvo and M. M. Millonas, ”How swarms build cognitive maps,” NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES, vol. 144, pp. 439-439, 1995.
  • [17] T. Niknam, R. Khorshidi, and B. B. Firouzi, ”A hybrid evolutionary algorithm for distribution feeder reconfiguration,” Sadhana, vol. 35, pp. 139-162, 2010.
  • [18] H. Cao, P. Huang, and S. Luo, ”A novel image segmentation algorithm based on artificial ant colonies,” Medical Imaging and Informatics, pp. 63-71, 2008.
  • [19] P. Huang, H. Cao, and S. Luo, ”An artificial ant colonies approach to medical image segmentation,” Computer Methods and Programs in Biomedicine, vol. 92, pp. 267-273, 2008.
  • [20] S. A. Etemad and T. White, ”An ant-inspired algorithm for detection of image edge features,” Applied Soft Computing, vol. 11, pp. 4883-4893, 2011.
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