Skip to main content
SpringerLink
Log in

High performance FPGA-based image correlation

  • Special Issue
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Image correlation is widely used for image and picture processing. Typical applications of image correlation are object location, image registration and sub-image similarity measurement. However, image correlation requires the comparison of a large number of sub-images implying a large computational effort that may prevent its use for real-time applications. On the other hand, correlation computation is very well suited for FPGA implementations. In this work we present efficient architectures for the implementation of Zero-Mean Normalized Cross-Correlation using FPGAs with application to image correlation. In particular, we compare the implementations of correlation in the spatial and spectral domains. Experimental results demonstrate that FPGAs improve performance by at least two orders of magnitude with respect to software implementations on a modern personal computer. This speed-up makes the performance of correlation computation suitable for real-time image processing. The proposed architectures have been applied to a correlation-based fingerprint-matching algorithm, demonstrating that real-time processing requirements can be well satisfied with an FPGA-based implementation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. This time does not include other tasks, such as image capturing and transferring. The target time for a complete transaction, including all tasks is 2.5 s.

References

  1. Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. 24, 325–376 (1992)

    Article  Google Scholar 

  2. Cavadini, M., Wosnitza, M., Tröster, G.: Multiprocessor system for high-resolution image correlation in real time. IEEE Trans. Very Large Scale Integr. VLSI Syst. 9(3), 439–449 (2001)

    Article  Google Scholar 

  3. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  4. Virtex-4 Family Overview: Xilinx Inc. http://www.xilinx.com (2004)

  5. Borgioli, R.C.: Fast Fourier transform correlation versus direct discrete time correlation. In: Proceedings of the IEEE, pp. 1602–1604 (1968)

  6. Test Plan For Secure Biometrics Match-on-Card (sBMOC): National Institute of Standards and Technology (NIST) (2007)

  7. Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC 2002: second fingerprint verification competition. In: 16th Proc. Int. Conf. Pattern Recognition, vol. 3, pp. 811–814 (2002)

  8. Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC 2004: Third fingerprint verification competition. In: Proc. Int. Conf. on Biometric Authentication (2004)

  9. van der Heijden, F.: Image Based Measurement Systems. Wiley, Chichester (1994)

    Google Scholar 

  10. Gonzales, R.C., Woods, R.E: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  11. Crouzil, A., Massip-Pailhes, L., Castan, S.: A new correlation criterion based on gradient fields similarity. In: Proc. 13th Int. Conf. on Pattern Recognition, pp. 632–636 (1996)

  12. Siegel, L.J., Siegel, H.J., Feather, A.E.: Parallel processing approaches to image correlation. IEEE Trans. Comput. C-31(3), 208–218 (1982)

    Article  Google Scholar 

  13. Systems and methods with identity verification by streamlined comparison and interpretation of fingerprints and the like. US Patent 6356649 (2002)

  14. De Castro, E., Morando, C.: Registration of translated and rotated images using finite Fourier Transforms. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9, 5(Sept.), pp. 700–703

  15. Sujan V.A., Mulqueen M.P.: Fingerprint identification using space invariant transforms. Pattern Recognit. Lett. 23(5), 609–619 (2002)

    Article  MATH  Google Scholar 

  16. Derrode, S., Ghorbel, F.L.: Robust and efficient Fourier-Mellin transform approximations for Gray-level image reconstruction and complete invariant description. Comput. Vis. Image Underst. 83(1), 57–78 (2001)

    Article  MATH  Google Scholar 

  17. XtremeDSP for Virtex-4 FPGAs User Guide: Xilinx Inc. http://www.xilinx.com (2007)

  18. Block RAM (BRAM) Block (v1.00a): Xilinx Inc. http://www.xilinx.com (2004)

  19. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  20. Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)

    Article  Google Scholar 

  21. Kovacs-Vajna, Z.M.: A Fingerprint Verification System based on triangular matching and dynamic time warping. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1266–1276 (2000)

    Article  Google Scholar 

  22. Huvanandana, S., Kim, C., Hwang, J.-N.: Reliable and fast fingerprint identification for security applications. Int. Conf. Image Process. 2000 Proc. 2, 503–506 (2000)

    Google Scholar 

  23. Bazen, A.M., Verwaaijen, G.T.B., Gerez, S.H., Veelenturf, L.P.J., Van der Zwaag, B.J.: A correlation-based fingerprint verification system. In: Proc. Workshop on Circuits, Systems and Signal Processing (ProRISC 2000), pp. 205–213 (2000)

  24. Fast Fourier Transform v3.0: Xilinx LogiCore, Xilinx Inc. http://www.xilinx.com (2004)

  25. Maio D., Maltoni D., Cappelli R., Wayman J.L., Jain A.K.: FVC2000: fingerprint verification competition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 402–412 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electronic Technology Department, University Carlos III of Madrid, Butarque 15, Leganes, 28911, Madrid, Spain

    Almudena Lindoso & Luis Entrena

Authors
  1. Almudena Lindoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis Entrena
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Almudena Lindoso.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lindoso, A., Entrena, L. High performance FPGA-based image correlation. J Real-Time Image Proc 2, 223–233 (2007). https://doi.org/10.1007/s11554-007-0066-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-007-0066-5

Keywords

Search

Navigation