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Description of Interest Regions with Center-Symmetric Local Binary Patterns

  • Marko Heikkilä
  • Matti Pietikäinen
  • Cordelia Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the center-symmetric local binary pattern (CS-LBP) descriptor. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas, and computational efficiency. We evaluate our descriptor using a recently presented test protocol. Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.

Keywords

Local Binary Pattern Illumination Change JPEG Compression Gradient Magnitude Sift Descriptor 
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 2006

Authors and Affiliations

  • Marko Heikkilä
    • 1
  • Matti Pietikäinen
    • 1
  • Cordelia Schmid
    • 2
  1. 1.Machine Vision Group, Infotech Oulu and Department of Electrical and Information EngineeringUniversity of OuluFinland
  2. 2.INRIA Rhône-AlpesMontbonnotFrance

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