Abstract
Particle Swarm Optimization (PSO) is an evolutionary computation technique frequently used for optimization tasks. This work aims at applying PSO for recognizing specific patterns in complex images. Experiments were done with gray level and color images, with and without noise. PSO was able to find predefined reference images, submitted to translation, rotation, scaling, occlusion, noise and change in the viewpoint in the landscape image. Several experiments were done to evaluate the performance of PSO. Results show that the proposed method is robust and very promising for real-world applications.
This work was partially supported by the Brazilian National Research Council – CNPq, under research grants nos. 309262/2007-0 to H.S. Lopes, and 477922/06-6 to T.M. Centeno. Authors also acknowledge the financial aid from CAPES as a scholarship to H.A. Perlin.
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References
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill, New York (1995)
Felizberto, M.K., Centeno, T.M., Lopes, H.S.: Object detection for computer vision using a robust genetic algorithm. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 284–293. Springer, Heidelberg (2005)
Felizberto, M.K., Lopes, H.S., Centeno, T.M., Arruda, L.V.R.: An object detection and recognition system for weld bead extraction from digital radiographs. Comput. Vis. Image Unders. 102, 238–249 (2006)
Guerra, C., Pascucci, V.: Find line segments with tabu search. IEICE Trans. Inf. Syst. E84-D(12), 1739–1744 (2001)
Eberhart, R.C., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Hembecker, F., Lopes, H.S., Godoy Jr., W.: Particle swarm optimization for the multidimensional knapsack problem. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPA Workshops 2006. LNCS, vol. 4331, pp. 358–365. Springer, Heidelberg (2006)
Lopes, H.S., Coelho, L.S.: Particle swarm optimization with fast local search for the blind traveling salesman problem. In: Proc. 5th Int. Conf. on Hybrid Intelligent Systems, pp. 245–250 (2005)
Talbi, H., Batouche, M.: Hybrid particle swam with differential evolution for multimodal image registration. In: Proc. IEEE Int. Conf. on Industrial Technology (ICIT), pp. 1568–1572 (2004)
Wachowiak, M.P., Smolíková, R., Zurada, J.M., Elmaghraby, E.: An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans. Evolut. Comput. 8(3), 289–301 (2004)
Maruo, M.H., Lopes, H.S., Delgado, M.R.B.S.: Self-Adapting Evolutionary Parameters: Encoding Aspects for Combinatorial Optimization Problems. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 154–165. Springer, Heidelberg (2005)
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Perlin, H.A., Lopes, H.S., Centeno, T.M. (2008). Particle Swarm Optimization for Object Recognition in Computer Vision. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_2
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DOI: https://doi.org/10.1007/978-3-540-69052-8_2
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