Advertisement

Template Matching Using a Physical Inspired Algorithm

  • Diego OlivaEmail author
  • Erik Cuevas
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 117)

Abstract

Template matching (TM) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. In a TM approach, it is sought the point in which it is proposed the best possible resemblance between a sub-image known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method finds the best possible coincidence between the images through an exhaustive computation of the Normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). In this chapter, a new algorithm based on the Electromagnetism-Like algorithm (EMO) is presented to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version where a modification of the local search procedure is incorporated in order to accelerate the exploitation process. The number of NCC evaluations is also reduced by considering a memory which stores the NCC values previously visited in order to avoid the re-evaluation of the same search locations (particles).. Conducted simulations show that the proposed method achieves the best balance over other TM algorithms, in terms of estimation accuracy and computational cost.

Keywords

Particle Swarm Optimization Local Search Differential Evolution Template Match Local Search Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, New York (2009)Google Scholar
  2. 2.
    Crispin, A.J., Rankov, V.: Automated inspection of PCB components using a genetic algorithm template-matching approach. Int. J. Adv. Manuf. Technol. 35, 293–300 (2007)CrossRefGoogle Scholar
  3. 3.
    Juan, L., Jingfeng, Y., Chaofeng, G.: Research and implementation of image correlation matching based on evolutionary algorithm. In: Future Computer Science and Education (ICFCSE). 2011 International Conference, pp. 499, 501. 20–21 Aug 2011Google Scholar
  4. 4.
    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. Cybernet. 2(1), 120–147 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Liu, F., Duana, H., Deng, Y.: A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching. Optik 123, 1955–1960 (2012)CrossRefGoogle Scholar
  8. 8.
    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, 535–549 (2009)CrossRefGoogle Scholar
  9. 9.
    Duan, H., Chunfang, X., Liu, S., Shao, S.: Template matching using chaotic imperialist competitive algorithm. Pattern Recogn. Lett. 31, 1868–1875 (2010)CrossRefGoogle Scholar
  10. 10.
    Ilker, B., Birbil, S., Shu-Cherng, F.: An electromagnetism-like mechanism for global optimization. J. Global Optim. 25, 263–282 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Birbil, S.I., Fang, S.C., Sheu, R.L.: On the convergence of a population-based global optimization algorithm. J. Global Optim. 30(2), 301–318 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Rocha, A., Fernandes, E.: Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems. Int. J. Comput. Math. 86, 1932–1946 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Rocha, A., Fernandes, E.: Modified movement force vector in an electromagnetism-like mechanism for global optimization. Optim. Methods Softw. 24, 253–270 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Naderi, B., Tavakkoli-Moghaddam, R., Khalili, M.: Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan. Knowl.-Based Syst. 23, 77–85 (2010)CrossRefGoogle Scholar
  15. 15.
    Hung, H.-L., Huang, Y.-F.: Peak to average power ratio reduction of multicarrier transmission systems using electromagnetism-like method. Int. J. Innovative Comput. 7(5), 2037–2050 (2011)Google Scholar
  16. 16.
    Yurtkuran, A., Emel, E.: A new hybrid electromagnetism-like algorithm for capacitated vehicle routing problems. Expert Syst. Appl. 37, 3427–3433 (2010)CrossRefGoogle Scholar
  17. 17.
    Jhen-Yan, J., Kun-Chou, L.: Array pattern optimization using electromagnetism-like algorithm. AEU Int. J. Electron. Commun. 63, 491–496 (2009)CrossRefGoogle Scholar
  18. 18.
    Wu, P., Wen-Hung, Y., Nai-Chieh, W.: An electromagnetism algorithm of neural network analysis an application to textile retail operation. J. Chin. Inst. Ind. Eng. 21, 59–67 (2004)Google Scholar
  19. 19.
    Lee, C.H., Chang, F.K.: Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Syst. Appl. 37, 8871–8878 (2010)CrossRefGoogle Scholar
  20. 20.
    Cuevas, E., Oliva, D., Zaldivar, D., Pérez-Cisneros, M., Sossa, H.: Circle detection using electro-magnetism optimization. Inf. Sci. 182(1), 40–55 (2012)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Guan, Xianping, Dai, Xianzhong, Li, Jun: Revised electromagnetism-like mechanism for flow path design of unidirectional AGV systems. Int. J. Prod. Res. 49(2), 401–429 (2011)CrossRefGoogle Scholar
  22. 22.
    Lee, C.H., Chang, F.K.: Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Syst. Appl. 37, 8871–8878 (2010)CrossRefGoogle Scholar
  23. 23.
    Zhang, C., Li, X., Gao, L., Wu, Q.: An improved electromagnetism-like mechanism algorithm for constrained optimization. In: Expert Systems with Applications. doi: 10.1016/j.eswa.2013.04.028 (in Press)
  24. 24.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway. pp. 1942–1948 (1995)Google Scholar
  25. 25.
    Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, New York (2005)zbMATHGoogle Scholar
  26. 26.
    Pedersen, M.E.H.: Good parameters for Particle Swarm Optimization. Technical report HL1001, Hvass Laboratories (2010)Google Scholar
  27. 27.
    Pedersen, M.E.H.: Good parameters for Differential Evolution. Technical report HL1002, Hvass Laboratories (2010)Google Scholar
  28. 28.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Garcia, S., Molina, D., Lozano, M., Herrera, F.: 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. J. Heurist. (2008). doi: 10.1007/s10732-008-9080-4

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Departamento de Electrónica, CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  2. 2.Tecnológico de Monterrey, Campus GuadalajaraZapopanMexico

Personalised recommendations