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Unidimensional Multiscale Local Features for Object Detection Under Rotation and Mild Occlusions

  • Michael Villamizar
  • Alberto Sanfeliu
  • Juan Andrade Cetto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4478)

Abstract

In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the present one uses a local feature description that allows small changes in the histogram signature, giving robustness to partial occlusions. Local features over the object histogram are extracted during a Boosting learning phase, selecting the most discriminant features within a training histogram image set. The Integral Histogram has been used to compute local histograms in constant time.

Keywords

Local Feature Object Detection Patch Image Color Histogram Integral Image 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Villamizar
    • 1
  • Alberto Sanfeliu
    • 1
  • Juan Andrade Cetto
    • 1
  1. 1.Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Llorens Artigas 4-6, 08028 BarcelonaSpain

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