Abstract
For a wide-range of image processing tasks (such as feature tracking, object recognition, stereo matching and remote sensing), Template Matching (TM) serves as a crucial strategy to allow the localization and recognition of objects and/or patterns within digital images. The purpose of a TM approach is to find the location (region) within a source image which manifest the best possible resemblance to a given sub-image (commonly known as image template). Any TM algorithm (independently of their particularities) incorporates two important elements: 1. a search strategy; and 2. a similarity measurement. In its simplest form, a TM method involves exhaustive search processes were a similarity measurement (such as the Normalized Cross-Correlation) is applied for each available location within the source image. Intuitively, such an extensive amount of function evaluations has a strong impact on the algorithms computational cost, an issue that happens to be extremely detrimental for many real-world applications. In the last few years, Evolutionary Algorithms (EAs) have been proposed as an alternative to aid on the search process of TM approaches by reducing the amount of locations that are evaluated within a given source image; however, it is known that many of these methods carry with them several critical flaws which could negatively impact the TM process, including an insufficient exploration of the source image (which often leads to premature convergence). In this chapter, the swarm optimization algorithm known as Locust Search (LS) is applied to aid on solving the of template matching. The LS method includes a series of unique evolutionary operators that allows to explicitly avoid the concentration of search agents toward the best-known solutions, which in turn allows these agents to better explore of the available image’s search region. According to a series of experimental results, when compared to other TM approaches which integrates EAs as a part of their search strategy, LS achieves the best between estimation accuracy and computational load.
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References
Brunelli, R.: Template matching techniques in computer vision: theory and practice. Wiley, London (2009). ISBN 978-0-470-51706-2
Hadi, G., Mojtaba, L., Hadi, S.Y.: An improved pattern matching technique for lossy/lossless compression of binary printed Farsi and Arabic textual images. Int. J. Intell. Comput. Cybern 2(1), 120–147 (2009)
Krattenthaler, W., Mayer, K.J., Zeiler, M.: Point correlation: a reduced-cost template matching technique. In: Proceedings of the First IEEE International Conference on Image Processing, pp. 208–212 (1994)
Rosenfeld, A., VanderBrug, G.J.: Coarse-fine template matching. IEEE Trans. Syst. Man Cybern. SMC-7(2), 104–107 (1977)
Tanimoto, S.L.: Template matching in pyramids. Comput. Vision Graph. Image Proc. 16(4), 356–369 (1981)
Dong, N., Wu, C.-H., Ip, W.-H., Chen, Z.-Q., Chan, C.-Y., Yung, K.-L.: An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection. Expert Syst. Appl. 38, 15172–15182 (2011)
Mitchell, M.: An Introduction to Genetic Algorithms. Cambridge, MA (1996)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1248 (1995)
Haibin, D., Chunfang, X., Senqi, L., Shan, S.: Template matching using chaotic imperialist competitive algorithm. Pattern Recogn. Lett. 31, 1868–1875 (2010)
Chen, G., Low, C.P., Yang, Z.: Preserving and exploiting genetic diversity in evolutionary programming algorithms. IEEE Trans. Evol. Comput. 13(3), 661–673 (2009)
Adra, S.F., Fleming, P.J.: Diversity management in evolutionary many-objective optimization. IEEE Trans. Evol. Comput. 15(2), 183–195 (2011)
Tan, K.C., Chiam, S.C., Mamun, A.A., Goh, C.K.: Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur. J. Oper. Res. 197, 701–713 (2009)
Cuevas, E., González, A., Zaldívar, D., Pérez-Cisneros, M.: An optimisation algorithm based on the behaviour of locust swarms. Int. J. Bio-Inspir. Comput. 7(6), 402–407 (2015)
Sadoghi Yazdi, H.: An improved pattern matching technique for lossy/lossless compression of binary printed farsi and arabic textual images. Int. J. Intell. Comput. Cybern., 120–147 (2009)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Engine, Engineering Faculty, Computer Engineering Department, Erciyes University (2005)
Hossein, A., Hossein-Alavi, A.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17, 4831–4845 (2012)
Cuevas, E., Echavarría, A., Zaldívar, D., Pérez-Cisneros, M.: A novel evolutionary algorithm inspired by the states of matter for template matching. Expert Syst. Appl. 40, 6359 (2013)
Topaz, C.M., Bernoff, A.J., Logan, S., Toolson, W.: A model for rolling swarms of locusts. Eur. Phys. J. Spec. Top. 157, 93–109 (2008)
Topaz, C.M., D’Orsogna, M.R., Edelstein-Keshet, L., Bernoff, A.J.: Locust dynamics: behavioral phase change and swarming. Plos Comput. Biol. 8(8), 1–11
Cuevas, E., González, A., Fausto, F., Zaldívar, D., Pérez-Cisneros, M.: Multithreshold segmentation by using an algorithm based on the behavior of locust swarms. Math. Probl. Eng. 2015, 25 (2015). Article ID 805357. https://doi.org/10.1155/2015/805357
Dong, N., Wu, C.-H., Ip, W.-H., Chen, Z.-Q., Chan, C.-Y., Yung, K.-L.: An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection. Expert Syst. Appl. 38(12), 15172–15182 (2011)
Liu, F., Duan, H., Deng, Y.: A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching. Optik-Int. J. Light Electron Opt. 123(21), 1955–1960 (2012)
Wu, C.-H., Wang, D.-Z., Ip, A., Wang, D.-W., Chan, C.-Y., Wang, H.-F.: A particle swarm optimization approach for components placement inspection on printed circuit boards. J. Intell. Manuf. 20(5), 535–549 (2009)
Chen, G., Low, C.P., Yang, Z.: Preserving and exploiting genetic diversity in evolutionary programming algorithms. IEEE Trans. Evol. Comput. 13(3), 661–673 (2009)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)
Garcia, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of nonparametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special session on real parameter optimization. J. Heuristics (2008)
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Appendix
Appendix
Images dataset used on our experimental setup. a Waldo 1, b Circuit board 1, c Satellite image, d Waldo 2, e Circuit board 2, and f Circuit board 3.
Image | Template | NCC surface | Image resolution | |
|---|---|---|---|---|
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| 768 × 1024 |
(b) |
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| 474 × 700 |
(c) |
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| 641 × 1483 |
(d) |
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| 768 × 1024 |
(e) |
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| 2137 × 3027 |
(f) |
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| 2448 × 3264 |
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Cuevas, E., Fausto, F., González, A. (2020). Locust Search Algorithm Applied for Template Matching. In: New Advancements in Swarm Algorithms: Operators and Applications. Intelligent Systems Reference Library, vol 160. Springer, Cham. https://doi.org/10.1007/978-3-030-16339-6_10
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DOI: https://doi.org/10.1007/978-3-030-16339-6_10
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