Skip to main content
SpringerLink
Log in

Brand identification using Gaussian derivative histograms

  • Special issue on ICVS 2003
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract.

In this article, we describe a module for the identification of brand logos from video data. A model for the visual appearance of each logo is generated from a small number of sample images using multidimensional histograms of scale-normalized chromatic Gaussian receptive fields. We compare several identification techniques based on multidimensional histograms. Each of the methods displays high recognition rates and can be used for logo identification. Our method for calculating scale-normalized Gaussian receptive fields has linear computational complexity and is thus well adapted to a real-time system. However, with the current generation of microprocessors we obtain at best only two images per second when processing a full PAL video stream. To accelerate the process, we propose an architecture that combines fast detection, reliable identification, and fast tracking for speedup. The resulting real-time system is evaluated using video streams from sports Formula 1 races and football.

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

Similar content being viewed by others

References

  1. Agarwal S, Roth D (2002) Learning a sparse reprsentation for object detection. In: European conference on computer vision, pp 113-130

  2. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape context. IEEE Trans Pattern Anal Mach Intell 24(4):509-522

    Article  Google Scholar 

  3. Crowley JL, Riff O, Piater J (2002) Fast computation of characteristic scale using a half octave pyramid. In: Workshop on cognitive computing, Zurich, Switzerland

  4. Freeman WT, Pasztor EC (1999) Learning low-level vision. In: International conference on computer vision, pp 1182-1189

  5. Hall D, Colin de Verdiére V, Crowley JL (2000) Object recognition using coloured receptive fields. In: European conference on computer vision, pp 164-177 Dublin, Ireland

  6. Lades M, Vorbrüggen JC, Buhmann J, Lange J, von der Mahlsburg C, Würz RP, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. Trans Comput 42(3):300-311

    Article  Google Scholar 

  7. Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vision 30(2):79-116

    Article  Google Scholar 

  8. Lowe DG (1999) Object recognition from local scale-invariant features. In: International conference on computer vision, pp 1150-1157

  9. Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: IEEE conference on computer vision and pattern recognition, Puerto Rico, pp 130-136

  10. Rao RPN, Ballard DH (1995) An active vision architecture based on iconic representations. Artif Intell 78(1-2):461-505

  11. Schiele B, Crowley JL (2000) Recognition without correspondence using multidimensional receptive field histograms. Int J Comput Vision 36(1):31-50

    Article  Google Scholar 

  12. Schiele B, Pentland A (1999) Probabilistic object recognition and localization. In: International conference on computer vision, Corfu, Greece, pp 177-182

  13. Schmid C, Mohr R (1997) Local greyvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell 19(5):530-534

    Article  Google Scholar 

  14. Schwerdt K, Crowley JL (2000) Robust face tracking using color. In: International conference on automatic face and gesture recognition, pp 90-95

  15. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vision 7(1):11-32

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. PRIMA Group, Laboratory GRAVIR-IMAG, INRIA Rhône-Alpes, 38330, St. Ismier, France

    Daniela Hall, Fabien Pélisson, Olivier Riff & James L. Crowley

Authors
  1. Daniela Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabien Pélisson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olivier Riff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James L. Crowley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniela Hall.

Additional information

Published online: 6 October 2004

James L. Crowley: This research is funded by the European Commission’s IST project DETECT (IST-2001-32157).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hall, D., Pélisson, F., Riff, O. et al. Brand identification using Gaussian derivative histograms. Machine Vision and Applications 16, 41–46 (2004). https://doi.org/10.1007/s00138-004-0146-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-004-0146-5

Keywords

Search

Navigation