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Using Local Integral Invariants for Object Recognition in Complex Scenes

  • Alaa Halawani
  • Hashem Tamimi
  • Hans Burkhardt
  • Andreas Zell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)

Abstract

This paper investigates the use of local descriptors that are based on integral invariants for the purpose of object recognition in cluttered scenes. Integral invariants capture the local structure of the neighborhood around the points where they are computed. This makes them very well suited for constructing highly-discriminative local descriptors. The features are by definition invariant to Euclidean motion. We show how to extend the local features to be scale invariant. Regarding the robustness to intensity changes, two types of kernels used for extracting the feature vectors are investigated. The effect of the feature vector dimensionality and the performance in the presence of noise are also examined. Promising results are obtained using a dataset that contains instances of objects that are viewed in difficult situations that include clutter and occlusion.

Keywords

Feature Vector Object Recognition Recognition Rate Local Binary Pattern Interest Point 
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

  • Alaa Halawani
    • 1
  • Hashem Tamimi
    • 2
  • Hans Burkhardt
    • 1
  • Andreas Zell
    • 2
  1. 1.Chair of Pattern Recognition and Image ProcessingUniversity of FreiburgFreiburgGermany
  2. 2.Computer Science Dept.University of TübingenTübingenGermany

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