Skip to main content

A novel image matching algorithm based on sliding histograms of oriented gradients

Journal of Communications Technology and Electronics volume 59, pages 1446–1450 (2014)Cite this article

Abstract

A novel algorithm for image matching based on recursive calculation of histograms of oriented gradients over several circular sliding windows and pyramidal image decomposition is presented. The algorithm gives good results for geometrically distorted and scaled scene images. The results of computer simulation obtained with the proposed algorithm are compared to those of available algorithms in terms of matching accuracy and processing time.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

References

  1. D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. 7th Int. Conf. on Computer Vision, Crete, 1999 (IEEE, New York, 1999), Vol. 2, pp. 1150–1157.

    Google Scholar 

  2. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” Comput. Vis. Image Underst. 110, 346–359 (2008).

    Article  Google Scholar 

  3. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” in Proc. IEEE Int. Conf. on Computer Vision, Barcelona, 2011 (IEEE, New York, 2011), pp. 2564–2571.

    Chapter  Google Scholar 

  4. M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: binary robust independent elementary features,” in Proc. 11th Eur. Conf. on Computer Vision. (ECCV’10), Hersonissos Heraklion Crete, Greece, 2010 (Springer-Verlag, 2010), pp. 778–792.

    Google Scholar 

  5. R. Ortiz, “FREAK: Fast Retina Keypoint,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’12, Providence, RI, June, 2012 (IEEE, New York, 2012), pp. 510–517.

    Google Scholar 

  6. V. H. Díaz-Ramírez and V. Kober, “Adaptive phaseinput joint transform correlator,” Appl. Opt. 46, 6543–6551 (2007).

    Article  Google Scholar 

  7. Y. Ouerhani, M. Jridi, and A. Alfalou, and C. Brosseau, “Optimized preprocessing input plane GPU implementation of an optical face recognition technique using a segmented phase only composite filter,” Opt. Commun. 289, 33–44 (2013).

    Article  Google Scholar 

  8. K. L. Rice, T. M. Taha, A. M. Chowdhury, A. A. S. Awwal, and D. L. Woodard, “Design and acceleration of phase-only filter-based optical pattern recognition for fingerprint identification,” Opt. Eng. 48(11), 117–206 (2009).

    Google Scholar 

  9. B. A. Zalesky and P. V. Lukashevich, “Scale invariant algorithm to match regions on aero or satellite images,” Proc. Pattern Recogn. Inf. Process. 11, 25–30 (2011).

    Google Scholar 

  10. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Comput. Vis. Pattern Recogn. 1, 886–893 (2005).

    Google Scholar 

  11. W. K. Pratt, Digital Image Processing (Wiley, New York, 2007).

    Book  Google Scholar 

  12. T. Lindeberg, “Scale-space theory: A basic tool for analysis structures at different scales,” J. Appl. Statis. 21, 225–270 (1994).

    Article  Google Scholar 

  13. B. Liu and A. Zaccarin, “New fast algorithms for the estimation of block motion vectors,” IEEE Trans. Circuits Syst. Video Technol. 3, 148–157 (1993).

    Article  Google Scholar 

  14. J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, International Journal of Computer Vision, 2005, vol. 61, no. 1, pp. 103–112, http://staff.science.uva.nl/aloi/

    Article  Google Scholar 

  15. Li X. Rong and V. P. Jilkov, “Survey of maneuvering target tracking. Part I: Dynamic models,” IEEE Trans. Aerosp. Electron. Syst. 39, 1333–1364 (2003).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, CICESE, Carretera Ensenada-Tijuana 3918, Zona Playitas, Ensenada B.C., 22860, Mexico

    D. Miramontes-Jaramillo & V. I. Kober

  2. Instituto Politécnico Nacional - CITEDI, Ave. del Parque 1310, Mesa de Otay, Tijuana B.C., 22510, Mexico

    V. H. Díaz-Ramírez

  3. Institute for Information Transmission Problems, Russian Academy of Sciences, Bol’shoi Karetnyi per. 19, str. 1, Moscow, 127994, Russia

    V. I. Kober & V. N. Karnaukhov

Authors
  1. D. Miramontes-Jaramillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. I. Kober
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. H. Díaz-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. N. Karnaukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. N. Karnaukhov.

Additional information

Original Russian Text © D. Miramontes-Jaramillo, V.I. Kober, V.H. Díaz-Ramírez, V.N. Karnaukhov, 2014, published in Informatsionnye Protsessy, 2014, Vol. 14, No. 1, pp. 56–63.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Miramontes-Jaramillo, D., Kober, V.I., Díaz-Ramírez, V.H. et al. A novel image matching algorithm based on sliding histograms of oriented gradients. J. Commun. Technol. Electron. 59, 1446–1450 (2014). https://doi.org/10.1134/S1064226914120146

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226914120146

Keywords

  • image matching
  • fast algorithm
  • histogram of oriented gradients
  • circular window