Skip to main content
Log in

A Configurable Circuit for Cross-Correlation in Real-Time Image Matching

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Cross-correlation (CC) is the most time-consuming in the implementation of image matching algorithms based on the correlation method. Therefore, how to calculate CC fast is crucial to real-time image matching. This work reveals that the single cascading multiply-accumulate (CAMAC) and concurrent multiply-accumulate (COMAC) architectures which have been widely used in the past, actually, do not necessarily bring about a satisfactory time performance for CC. To obtain better time performance and higher resource efficiency, this paper proposes a configurable circuit involving the advantages of CAMAC and COMAC for a large amount of multiply-accumulate (MAC) operations of CC in exhaustive search. The proposed circuit works in an array manner and can better adapt to changing size image matching in real-time processing. Experimental results demonstrate that this novel circuit which involves the two structures can complete vast MAC calculations at a very high speed. Compared with existing related work, it improves the computation density further and is more flexible to use.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Chen J Y, Hung K F, Lin H Y et al. Real-time FPGA-based template matching module for visual inspection application. In Proc. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, July 2012.

  2. Alam M S, Bal A. Improved multiple target tracking via global motion compensation and optoelectronic correlation. IEEE Trans. Industrial Electronics, 2007, 54(1): 522-529.

    Article  Google Scholar 

  3. Po LM, MaWC. A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circuits and Systems for Video Technology, 1996, 6(3): 313-317.

  4. Zhu S, Ma K K. A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. Image Processing, 2000, 9(2): 287-290.

    Article  Google Scholar 

  5. Mori M, Kashino K. Fast template matching based on normalized cross correlation using adaptive block partitioning and initial threshold estimation. In Proc. IEEE International Symposium on Multimedia (ISM), Dec. 2010, pp.196-203.

  6. Gao X Q, Duanmu C J, Zou C R. A multilevel successive elimination algorithm for block matching motion estimation. IEEE Trans. Image Processing, 2000, 9(3): 501-504.

    Article  Google Scholar 

  7. Li W, Salari E. Successive elimination algorithm for motion estimation. IEEE Trans. Image Processing, 1995, 4(1): 105-107.

    Article  Google Scholar 

  8. Lewis J P. Fast template matching. In Proc. Vision Interface, May 1995, pp.120-123.

  9. Viola P, Jones M. Robust real-time object detection. In Proc. International Workshop on Statistical & Computational Theories of Vision-modeling, Learning, Computing, and Sampling, Apr. 2001.

  10. Wu T, Toet A. Speed-up template matching through integral image based weak classifiers. Journal of Pattern Recognition Research, 2014, 9(1): 1-12.

    Google Scholar 

  11. Luo J, Konofagou E E. A fast normalized cross-correlation calculation method for motion estimation. IEEE Trans. Ultrasonics, Ferroelectrics, and Frequency Control, 2010, 57(6): 1347-1357.

  12. Tsai D M, Lin C T. Fast normalized cross correlation for defect detection. Pattern Recognition Letters, 2003, 24(15): 2625-2631.

    Article  Google Scholar 

  13. Luo J, Konofagou E E. A fast motion and strain estimation method. In Proc. IEEE Ultrasonics Symposium (IUS), Oct. 2010, pp.1608-1611.

  14. Goshtasby A, Gage S H, Bartholic J F. A two-stage cross correlation approach to template matching. IEEE Trans. Pattern Analysis and Machine Intelligence, 1984, 6(3): 374-378.

  15. Lindoso A, Entrena L, Lopze-Ongil C et al. Correlation-based fingerprint matching using FPGAs. In Proc. IEEE International Conference on Field Programmable Technology, Dec. 2005.

  16. Lindoso A, Entrena L. High performance FPGA-based image correlation. Journal of Real-Time Image Processing, 2007, 2(4): 223-233.

    Article  Google Scholar 

  17. Bailey D. Design for Embedded Image Processing on FPGAs. Wiley-IEEE Press, 2011, pp.299-301.

  18. Gupta N. A VLSI architecture for image registration in real time. IEEE Trans. Very Large Scale Integration (VLSI) Systems, 2007, 15(9): 981-989.

  19. Joanblanq C, Senn P, Colaitis M J. A 54-MHz CMOS programmable video signal processor for HDTV applications. IEEE Journal of Solid-State Circuits, 1990, 25(3): 730-734.

    Article  Google Scholar 

  20. Arambepola B, Patel V B, Cheung G. Cascadable one/two-dimensional digital convolver. IEEE Journal of Solid-State Circuits, 1988, 23(2): 351-357.

    Article  Google Scholar 

  21. Yang K M, Sun M T, Wu L. A family of VLSI designs for the motion compensation block-matching algorithm. IEEE Trans. Circuits and Systems, 1989, 36(10): 1317-1325.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Quan Zhou.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 117 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q., Yang, L. & Cao, H. A Configurable Circuit for Cross-Correlation in Real-Time Image Matching. J. Comput. Sci. Technol. 32, 1305–1318 (2017). https://doi.org/10.1007/s11390-017-1765-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-017-1765-4

Keywords

Navigation