Skip to main content
SpringerLink
Log in

Template Matching Using a Physical Inspired Algorithm

  • Chapter
  • First Online:
Advances and Applications of Optimised Algorithms in Image Processing

Part of the book series: Intelligent Systems Reference Library ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, New York (2009)

    Google Scholar 

  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)

    Article  Google Scholar 

  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 2011

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  9. Duan, H., Chunfang, X., Liu, S., Shao, S.: Template matching using chaotic imperialist competitive algorithm. Pattern Recogn. Lett. 31, 1868–1875 (2010)

    Article  Google Scholar 

  10. Ilker, B., Birbil, S., Shu-Cherng, F.: An electromagnetism-like mechanism for global optimization. J. Global Optim. 25, 263–282 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  13. Rocha, A., Fernandes, E.: Modified movement force vector in an electromagnetism-like mechanism for global optimization. Optim. Methods Softw. 24, 253–270 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. Yurtkuran, A., Emel, E.: A new hybrid electromagnetism-like algorithm for capacitated vehicle routing problems. Expert Syst. Appl. 37, 3427–3433 (2010)

    Article  Google Scholar 

  17. Jhen-Yan, J., Kun-Chou, L.: Array pattern optimization using electromagnetism-like algorithm. AEU Int. J. Electron. Commun. 63, 491–496 (2009)

    Article  Google Scholar 

  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. Lee, C.H., Chang, F.K.: Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Syst. Appl. 37, 8871–8878 (2010)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  22. Lee, C.H., Chang, F.K.: Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Syst. Appl. 37, 8871–8878 (2010)

    Article  Google Scholar 

  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. 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. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, New York (2005)

    MATH  Google Scholar 

  26. Pedersen, M.E.H.: Good parameters for Particle Swarm Optimization. Technical report HL1001, Hvass Laboratories (2010)

    Google Scholar 

  27. Pedersen, M.E.H.: Good parameters for Differential Evolution. Technical report HL1002, Hvass Laboratories (2010)

    Google Scholar 

  28. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)

    Article  MathSciNet  Google Scholar 

  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

Download references

Author information

Authors and Affiliations

  1. Departamento de Electrónica, CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva & Erik Cuevas

  2. Tecnológico de Monterrey, Campus Guadalajara, Zapopan, Jalisco, Mexico

    Diego Oliva

Authors
  1. Diego Oliva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diego Oliva .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Oliva, D., Cuevas, E. (2017). Template Matching Using a Physical Inspired Algorithm. In: Advances and Applications of Optimised Algorithms in Image Processing. Intelligent Systems Reference Library, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-48550-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48550-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48549-2

  • Online ISBN: 978-3-319-48550-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation