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Orientation Invariant Features for Multiclass Object Recognition

  • Michael Villamizar
  • Alberto Sanfeliu
  • Juan Andrade-Cetto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

We present a framework for object recognition based on simple scale and orientation invariant local features that when combined with a hierarchical multiclass boosting mechanism produce robust classifiers for a limited number of object classes in cluttered backgrounds. The system extracts the most relevant features from a set of training samples and builds a hierarchical structure of them. By focusing on those features common to all trained objects, and also searching for those features particular to a reduced number of classes, and eventually, to each object class. To allow for efficient rotation invariance, we propose the use of non-Gaussian steerable filters, together with an Orientation Integral Image for a speedy computation of local orientation.

Keywords

Feature Selection Object Recognition Object Class Integral Image 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

  • Michael Villamizar
    • 1
  • Alberto Sanfeliu
    • 1
  • Juan Andrade-Cetto
    • 2
  1. 1.Institut de Robòtica i Informàtica IndustrialUPC-CSICBarcelonaSpain
  2. 2.Computer Vision CenterUniversitat Autònoma de BarcelonaBellaterraSpain

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