Call for Paper - September 2019 Edition
IJCA solicits original research papers for the September 2019 Edition. Last date of manuscript submission is August 20, 2019. Read More

Genetic Algorithm Based Dot Pattern Image Processing

Print
PDF
IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)
© 2012 by IJCA Journal
ncipet - Number 14
Year of Publication: 2012
Authors:
Purshottam J. Assudani
Latesh G. Malik

Purshottam J Assudani and Latesh G Malik. Article: Genetic Algorithm Based Dot Pattern Image Processing. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) ncipet(14):31-35, March 2012. Full text available. BibTeX

@article{key:article,
	author = {Purshottam J. Assudani and Latesh G. Malik},
	title = {Article: Genetic Algorithm Based Dot Pattern Image Processing},
	journal = {IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)},
	year = {2012},
	volume = {ncipet},
	number = {14},
	pages = {31-35},
	month = {March},
	note = {Full text available}
}

Abstract

Dot pattern analysis and matching is necessary for many of the image analysis and pattern recognition problems. This paper uses local binary pattern for extracting the Dot pattern image features which is first pre-processed (Re-constructed, Rotated, Enhanced). It states that only the more discriminated features can be retained by discarding the less discriminated features using Genetic Algorithm. The optimized features thus obtained can be used for matching the two dot patterns for similarity using Euclidean Distance.

References

  • R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
  • O. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint,The MIT Press, 1993.
  • J.M. Chassery, A. Montanvert, Geometric Representation of Shapes and Objects for Visual Perception, Geometric Reasoning for Perception and Action, C. Laugier (Ed.), selected papers presented in a Workshop held at Grenoble, France, September 16–17, 1991, Springer–Verlag, pp. 163–182.
  • H. Ogawa, Labeled pattern matching by Delaunay triangulation and maximal cliques, Pattern Recognition 19 (1986) 35–40.
  • R. Laurini, D. Thompson, Fundamentals of Spatial Information Systems, The A.P.I.C. Series, No. 37, Academic Press, London, 1992.
  • P.J. Taylor, Quantitative Methods in Geography: An Introduction to Spatial Analysis, Houghton Mifflin Company, Boston, 1977.
  • A. Okabe, B. Boots, K. Sugihara, Spatial tessellations: Concepts andApplications of Voronoi Diagrams, John Wiley and Sons, 1992.
  • J. Sprinzak and M. Werman, Affine point matching, Pattern Recog. Letters, vol. 15, no. 4, pp. 337-339, 1994.
  • L. Zhang and W. Xu, Point-pattern matching using irreducible matrix and relative invariant, Tsinghua Sci. Tech., vol. 4, no. 4, pp. 1602-1605, 1999.
  • L. Zhang, W. Xu and C. Chang, Genetic algorithm for point pattern matching, Pattern Recog. Letters, vol. 24, pp. 9-19, 2003.
  • J.P. Starink and E. Backer, Finding point correspondences using simulated annealing, Pattern Recogn., vol. 28, pp. 231-240, 1995.
  • P.-Y. Yin, Particle swarm optimization for point pattern matching, J.Visual Commu. & Image, vol. 17, pp. 143-162, 2003.
  • M. Melanie, An introduction to Genetic Algorithms, Cambridge, Massachusetts, MIT Press, 1998.
  • T.Ojala, M.Pietik¨ ainen, T.T.M¨aenp¨a¨ a, Multiresolution gray-scale and rotation invariant texture classification with localbinarypattern, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (7) (2002) 971–987.
  • M. Kokare, P.K. Biswas, B.N. Chatterji, Rotation-invariant texture image retrieval using rotated complex wavelet filters, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 36 (6) (2006) 1273–1282.
  • V. Kyrki, J.K. Kamarainen, Simple Gabor feature space for invariant object recognition, Pattern Recognition Letter 25 (3) (2004) 311–318.
  • V. Kyrki, J.K. Kamarainen, Simple Gabor feature space for invariant object recognition, Pattern Recognition Letter 25 (3) (2004) 311–318.
  • N.G. Kingsbury, Rotation-invariant local feature matching with complex wavelets, in: 14th European Signal Processing Conference, 2006.
  • J. Adams, D. L. Woodard, G. Dozier, P. Miller, K. Bryant, and G. Glenn. Genetic-based type II feature extraction for periocular biometric recognition: Less is more. In Proc. Int. Conf. on Pattern Recognition, 2010. to appear.
  • Huang C. L. and Wang C. J. “GA-based feature selection and parameters optimization for support vector machines”,. C.-L. Huang, C.-J. Wang / Expert Systems with Applications. Vol. 31(2), 2006, pp231–240.
  • Adams, J., Woodard, D. L., Dozier, G., Miller, P., Glenn, G., Bryant, K. "GEFE: Genetic & Evolutionary Feature Extraction for Periocular- Based Biometric Recognition," Proceedings 2010 ACM Southeast Conference, April 15-17, 2010, Oxford, MS.
  • Dozier, G., Adams, J., Woodard, D. L., Miller, P., Bryant, K. "A Comparison of Two Genetic and Evolutionary Feature Selection Strategies for Periocular-Based Biometric Recognition via XTOOLSS", Proceedings of the 2010 International Conference on Genetic and Evolutionary Methods (GEM'10: July 12-15, 2010, Las Vegas, USA).
  • Simpson, L. , Dozier, G., Adams, J., Woodard, D. L., Dozier, G., Miller, P., Glenn, G., Bryant, K.. "GEC-Based Type-II Feature Extraction for Periocular Recognition via X-TOOLSS," Proceedings 2010 Congress on Evolutionary Computation, July 18-23, Barcelona, Spain
  • Dozier, G., Bell, D., Barnes, L., and Bryant, K. (2009). "Refining Iris Templates via Weighted Bit Consistency", Proceedings of the 2009 Midwest Artificial Intelligence & Cognitive Science (MAICS) Conference, Fort Wayne, April 18-19, 2009.
  • Dozier, G., Adams, J., Woodard, D. L., Miller, P., Bryant, K. "A Comparison of Two Genetic and Evolutionary Feature Selection Strategies for Periocular-Based Biometric Recognition via XTOOLSS", (to appear in) The Proceedings of the 2010 International Conference on Genetic and Evolutionary Methods (GEM'10: July 12- 15, 2010, Las Vegas, USA).
  • Tamirat Abegaz, Gerry Dozier, Kelvin Bryant Joshua Adams, Khary Popplewell, Joseph Shelton, ,Karl Ricanek, Damon L. Woodard” “Hybrid GAs for Eigen-Based Facial Recognition”, accepted for IEEE Symposium Series in Computational Intelligence 2011 (SSCI 2011)