Automatic construction of image operators using a genetic programming approach

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Emerson Carlos Pedrino and Jose Hiroki Saito and Edilson R. R. Kato and Orides Morandin, Jr. and Luis Mariano Del Val Cura and Valentin Obac Roda and Mario L. Tronco and Roberto H. Tsunaki: Automatic construction of image operators using a genetic programming approach. 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011), pp. 636-641, 22-24 November 2011.



This paper presents a methodology for automatic construction of image operators using a linear genetic programming approach, for binary, gray level and color image processing, where the processing solution for a particular application is expressed in terms of the basic morphological operators, dilation and erosion, in conjunction with convolution and logical operators. Genetic Programming (GP), based on concepts of genetics and Darwin's principle of natural selection, to genetically breed and evolve computer programs to solve a wide variety of problems, is a branch of evolutionary computation, and it is consolidating as a promising methodology to be used in applications involving pattern recognition, classification problems and modeling of complex systems. Mathematical morphology is based on the set theory (complete lattice), where the notion of order is very important. This processing technique has proved to be a powerful tool for many computer vision tasks. However, the manual design of complex operations involving image operators is not trivial in practice. Thus, the proposed methodology tries to solve these drawbacks. Some examples of applications are presented and the results are discussed and compared with other methods found in the literature.

Extended Abstract


Used References

E. R. Dougherty, An introduction to Morphological Image Processing, SPIE, Washington, 1992.

J. Serra, Image Analysis and Mathematical Morphology, Academic Press Inc, California, 1982.

A. R. Weeks Jr., Fundamentals of Electronic Image Processing, SPIE, Washington 1996.

P. Soille, Morphological Image Analysis, Principles and Applications, Springer-Verlag, Berlin, 1999.

S. Marshall, N. R. Harvey, D. Greenhalgh, Design of Morphological Filters using Genetic Algorithms, EUSIPCO, Tampere, Finland, September, 2000.

J. Facon, Morfologia Matemática: Teoria e Exemplos, Editora Universitária da Pontifícia Universidade Católica do Paraná, 1996.

F. Ortiz, F. Torres, E. De Juan, N. Cuenca, Colour Mathematical Morphology for Neural Image Analysis Real Time Imaging 8 (2002) 455-465.

P. Maragos, Lattice Image Processing: A Unification of Morphological and Fuzzy Algebraic Systems, Journal of Mathematical Imaging and Vision 22 (2005) 333-353.

M. I. Quintana, R. Poli, E. Claridge, Genetic programming for mathematical morphology algorithm design on binary images, In M. Proceedings of the International Conference KBCS, 2002, pp. 161-171.

J. Barrera, E.R. Dougherty, N.S. Tomita, Automatic programming of binary morphological machines by design of statistically optimal operators in the context of computational learning theory, Journal of Electronic Imaging 6(1) (1997) 54-67. (Pubitemid 127449866)

E. R. Dougherty, R. P. Loce, Eficient design strategies for the optimal binary digital morphological filter: probabilities, constraints, and structuring element libraries, Marcel Dekker, New York, 1993.

M. Schmitt, Mathematical morphology and artificial intelligence: An automatic programming system, Signal Processing, 16(4) (1989) 389-401.

J. Barrera, R. Terada, R. Hirata, N.S.T Hirata, Automatic programming of morphological machines by pac learning, Fundamenta Informaticae, 41(1-2) (2000) 229-258.

M. Yu, N. Eua-anant, A. Saudagar, L. Udpa, Genetic algorithm approach to image segmentation using morphological operations, In International Conference on Image Processing, 1998, pp. 775-779.

I. Yoda, K. Yamamoto, H. Yamada, Automatic acquisition of hierarchical mathematical morphology procedures by genetic algorithms, Image and Vision Computing, 17(10) (1999) 749-760.

J. Bala, H. Wechsler, Shape analysis using morphological processing and genetic Algorithms, In Proceedings of the 1991 IEEE International Conference on Tools with Artificial Intelligence TAI'91, Los Alamitos, CA., 1991, pp. 130-137.

N. R. Harvey, S. Marshall, The use of genetic algorithms in morphological filter Design, Signal Processing: Image Communication 8(1) (1996) 55-72.

R. M. Haralick, S. R. Sternberg, and X. Zhuang, Image analysis using mathematical morphology, IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4) (1987) 532-550. (Pubitemid 17632080)

J. Angulo, J. Serra, Morphological Coding of Color Images by Vector Connected Filters, Centre de Morphologie Mathématique, Ecole des Mines de Paris, 2005.

J. Chanussot, P. Lambert, Total ordering based on space filling curves for multivalued morphology, Proceedings of the 4th International Symposium on Mathematical Morphology and its Applications, Amsterdam, 1998, pp. 51-58.

M. I. Quintana Hernandez, Genetic Programming applied to Morphological Image Processing, PhD thesis, School of Computer Science, University of Birmingham, 2005.

D. Barrios, A. Carrascal, D. Manrique, J. Rios, Optimisation with real-coded genetic algorithms based on mathematical morphology, International Journal of Computer Mathematics, 80(3) (2003) 275-293. (Pubitemid 41715059)

D. Barrios, D. Manrique, J. Porras, Real-coded genetic algorithms based on mathematical morphology, Lecture Notes in Computer Science, 1876 (2001) 706-712.

T. Belpaeme, Evolution of visual feature detectors, In R. Poli, S. Cagnoni, H. M.Voigt, T. Fogarty, and P. Nordin, editors, Late Breaking Papers at EvoISAP'99: the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing, Goteborg, Sweden, 1999, pp. 1-10.

J. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1975.

J. Koza, Genetic Programming, MIT Press, 1992.

M. Bhattacharya, A. Abraham, B. Nath, A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria, In Proceedings of HIS, 2001, pp. 379-393.

M. Oltean, C. Grosan, M. Oltean, Encoding Multiple Solutions in a Linear Genetic Programming Chromosome, International Conference on Computational Science, 2004, 1281-1288.

D. Goldberg, K. Déb, A Comparative Analysis of Selection Schemes Used in Genetic Algorithms, In: Foundations of Genetic Algorithms [edited by G. Rawlins], Morgan-Kaufmann, 1991, pp. 69-93.

H. Miyao, Y. Nakano, Head and stem extraction from printed music scores using a neural network approach, ICDAR, 1995, pp. 1074-1079.

E. C. Pedrino, J. H. Saito, V. O. Roda, A genetic programming approach to reconfigure a morphological image processing architecture. International Journal of Reconfigurable Computing (Print), v. 2011, p. 712494-712503, 2010.

E. C. Pedrino, et al. Intelligent FPGA based system for shape recognition. In: VII IEEE SOUTHERN CONFERENCE ON PROGRAMMABLE LOGIC, 2011, Córdoba / Argentina. Universidade Nacional de Córdoba, p. 197-202, 2011.


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