El algoritmo de búsqueda armónica y sus usos en el procesamiento digital de imágenes
Harmony Search Algorithm and its Use in Digital Image Processing
Erik Cuevas, Noé Ortega-Sánchez
Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Guadalajara, Jalisco, México. erik.cuevas@cucei.udg.mx, noah55mx@gmail.com
]]>Article received on 01/10/2011
Accepted on 26/11/2012
Resumen
Métodos tradicionales de procesamiento de imagen presentan diferentes dificultades al momento de ser usados en imágenes que poseen ruido considerable y distorsiones. Bajo tales condiciones, el uso de técnicas de optimización se ha extendido en los últimos años. En este artículo se explora el uso del algoritmo de Búsqueda Armónica (BA) para el procesamiento digital de imágenes. BA es un algoritmo metaheurístico inspirado en la manera en que músicos buscan la armonía óptima en la composición musical, el cual ha sido empleado exitosamente para resolver problemas complejos de optimización. En este artículo se presenta dos problemas representativos del área de procesamiento digital de imágenes, como lo son: la detección de círculos y la estimación de movimiento, los cuales son planteados desde el punto de vista de optimización. Considerando este enfoque, en la detección de círculos se utiliza una combinación de tres puntos borde para codificar círculos candidatos. Utilizando las evaluaciones de una función objetivo (que determina si tales círculos están presentes en la imagen) el algoritmo de BA realiza una exploración eficiente hasta encontrar el circulo que mejor se aproxime a aquel contenido en la imagen (armonía óptima). Por otro lado, en la estimación de movimiento se utiliza el algoritmo de BA para encontrar el vector de movimiento que minimice la suma de diferencias absolutas entre bloques de dos imágenes consecutivas. Resultados experimentales muestran que las soluciones generadas son capaces de resolver adecuadamente los problemas planteados.
Palabras clave: Búsqueda armónica, detección de círculos, comparación de bloques, algoritmos meta-heurísticos, procesamiento digital de imágenes.
Abstract
Classical methods often face big difficulties in solving image processing problems when images contain noise and distortions. For such images, the use of optimization approaches has been extended. This paper explores application of the Harmony Search (HS) algorithm to digital image processing. HS is a meta-heuristic optimization algorithm inspired by musicians improvising new harmonies while performing. In this paper, we consider two tasks as examples: circle detection and motion estimation, both issues are approached as optimization problems. In such approach, circle detection uses a combination of three edge points as parameters to construct candidate circles. A matching function determines if such candidate circles are actually present in a given image. In motion estimation, the HS algorithm is used to find a motion vector that minimizes the sum of absolute differences between two consecutive images. Experimental results show that the generated solutions are able to properly solve the problems under consideration.
]]> Keywords: Harmony search, circle detection, block matching, meta-heuristics algorithms, digital image processing.
DESCARGAR ARTÍCULO EN FORMATO PDF
Referencias
1. Shilane, D., Martikainen, J., Dudoit, S., & Ovaska, S.J. (2008). A general framework for statistical performance comparison of evolutionary computation algorithm. Information Sciences, 178(14), 2870-2879. [ Links ]
2. Geem, Z.W., Kim, J.H., & Loganathan, G.V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68. [ Links ]
3. Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics Computation, 188(2), 1567-1579. [ Links ]
4. Lee, K.S. & Geem, Z.W. (2005). A new meta-heuristic algorithm for continuous engineering optimization, harmony search theory and practice. Computer Methods in Applied Mechanics Engineering, 194(36-38), 3902-3933. [ Links ]
5. Lee, K.S., Geem, Z.W., Lee, S.H., & Bae, K.-W. (2005). The harmony search heuristic algorithm for discrete structural optimization. Engineering Optimization, 37(7), 663-684. [ Links ]
6. Geem, Z.W. (2006). Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38(3), 259-280. [ Links ]
7. Lee, K.S. & Geem, Z.W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & Structures, 82(9-10), 781-798. [ Links ]
8. Ayvaz, M.T. (2007). Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm. Advances in Water Resources, 30(11), 2326-2338. [ Links ]
9. Geem, Z.W., Lee, K.S., & Park, Y. (2005). Application of harmony search to vehicle routing. American Journal of Applied Sciences, 2(12), 1552-1557. [ Links ]
10. Costa, L.F. & Cesar, R.M. (2001). Shape analysis and classification: theory and practice. Boca Raton, FL.: CRC Press. [ Links ]
11. Muammar, H. & Nixon, M. (1989). Approaches to extending the Hough transform. International Conference on Acoustics, Speech and Signal Processing (ICASSP-89), Glasgow, Scotland, 3, 1556-1559. [ Links ]
12. Atherton, T.J. & Kerbyson, D.J. (1993). Using phase to represent radius in the coherent circle Hough transform. IEE colloquium on Hough transforms. London, England, 5/1-5/4. [ Links ]
13. Shaked, D., Yaron, O., & Kiryati, N. (1994). Deriving stopping rules for the probabilistic Hough transform by sequential analysis. 12th IAPR International Conference on Pattern Recognition, Jerusalem, Israel, 2, 229-234. [ Links ]
14. Xu, L., Oja, E., & Kultanen, P. (1990). A new curve detection method: randomized Hough transform (RHT). Pattern Recognition Letters, 11(5), 331 -338. [ Links ]
15. Han, J.H., Koczy, L.T., & Poston, T. (1993). Fuzzy Hough transform. 2nd IEEE international Conference on Fuzzy Systems, San Francisco, CA, 2, 803-808. [ Links ]
16. Becker, J.M., Grousson, S., & Coltuc, D. (2002). From Hough Transforms to Integral Geometry. 2002 IEEE international geoscience and remote sensing symposium (IGARSS'02), Toronto, Canada, 3, 1444-1446. [ Links ]
17. Cuevas, E., Oliva, D., Zaldivar, D., Pérez, M., & Rojas, R. (2013). Circle Detection Algorithm Based on Electromagnetism-Like Optimization. Handbook of Optimization, Intelligent Systems Reference Library, 38, 907-934. DOI: 10.1007/978-3-642-30504-7. [ Links ]
18. Ayala-Ramirez, V., Garcia-Capulin, C.H., Perez-Garcia, A., & Sanchez-Yanez, R.E. (2006). Circle detection on images using genetic algorithms. Pattern Recognition Letters, 27(6), 652-657. [ Links ]
19. Dasgupta, S., Das, S., Biswas, A., & Abraham, A. (2010). Automatic circle detection on digital images whit an adaptive bacterial foraging algorithm. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 14(11), 1151-1164. doi:10.1007/s00500-009-0508-z. [ Links ]
20. Barron, J.L., Fleet, D.J., & Beauchemin, S.S. (1994). Systems and experiment performance of optical flow techniques, International Journal of Computer Vision, 12(1), 43-77. [ Links ]
21. Saha, A., Mukherjee, J., & Sural, S. (2008). New pixel-decimation patterns for block matching in motion estimation. Signal Processing: Image Communication, 23(10), 725-738. [ Links ]
22. Jong, H.-M., Chen, L.-G., & Chiueh, T.-D. (1994). Accuracy improvement and cost reduction of 3-step search block matching algorithm for video coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(1), 88-90. [ Links ]
23. Zhu, S. & Ma, K.-K. (2000). A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation. IEEE Transactions on Image Processing, 9(2), 287-290. [ Links ]
24. Nie, Y., & Ma, K.K. (2002). Adaptive Rood Pattern Search for Fast Block-Matching Motion Estimation. IEEE Transactions on Image Processing, 11(12), 1442-1449. [ Links ]
25. Yi-Ching, L., Jim, Z.C.L., & Zuu-Chang, H. (2009). Fast block matching using prediction and rejection criteria. Signal Processing, 89(6), 1115-1120. [ Links ]
26. Saha, A., Mukherjee, J., & Sural, S. (2011). A neighborhood elimination approach for block matching in motion estimation. Signal Processing: Image Communication, 26(8-9), 438-454. doi:10.1016/j.image.2011.06.002. [ Links ]
27. Yadav, P., Kumar, R., Panda, S.K., & Chang, C.S. (2012). An Intelligent Tuned Harmony Search Algorithm for Optimisation. Information Sciences, 196, 47-72. [ Links ]
28. Bresenham, J.E. (1987). A Linear Algorithm for Incremental Digital Display of Circular Arcs. Communications of the ACM, 20(2), 100-106. [ Links ]
29. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83. [ Links ]
30. Garcia, S., Molina, D., Lozano, M., & Herrera, F. (2008). A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special session on real parameter optimization. Journal of Heuristic, 15(6), 617-644. doi:10.1007/s10732-008-9080-4. [ Links ]
31. Santamaría, J., Cordón, O., Damas, S., García-Torres, J.M., & Quirin, A. (2008). Performance Evaluation of Memetic Approaches in 3D Reconstruction of Forensic Objects. Soft Computing, 13(8), 883-904. DOI: 10.1007/s00500-008-0351-7. [ Links ]
32. Saha, A., Mukherjee, J., & Sural, S. (2008). New pixel-decimation patterns for block matching in motion estimation. Signal Processing: Image Communication, 23(10), 725-738. [ Links ]
33. Yi-Ching, L., Jim, Z.C.L., & Zuu-Chang, H. (2009). Fast block matching using prediction and rejection criteria. Signal Processing, 89(6), 1115-1120. [ Links ]
]]>