Machine Vision and Applications

, Volume 16, Issue 1, pp 41–46 | Cite as

Brand identification using Gaussian derivative histograms

  • Daniela HallEmail author
  • Fabien Pélisson
  • Olivier Riff
  • James L. Crowley
Special issue on ICVS 2003


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.


Recognition Rate Current Generation Identification Technique Video Stream Video Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  • Daniela Hall
    • 1
    Email author
  • Fabien Pélisson
    • 1
  • Olivier Riff
    • 1
  • James L. Crowley
    • 1
  1. 1.PRIMA Group, Laboratory GRAVIR-IMAGINRIA Rhône-AlpesSt. IsmierFrance

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