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Perceptual Grouping Based on Iterative Multi-scale Tensor Voting

  • Leandro Loss
  • George Bebis
  • Mircea Nicolescu
  • Alexei Skourikhine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)

Abstract

We propose a new approach for perceptual grouping of oriented segments in highly cluttered images based on tensor voting. Segments are represented as second-order tensors and communicate with each other through a voting scheme that incorporates the Gestalt principles of visual perception. An iterative scheme has been devised which removes noise segments in a conservative way using multi-scale analysis and re-voting. We have tested our approach on data sets composed of real objects in real backgrounds. Our experimental results indicate that our method can segment successfully objects in images with up to twenty times more noise segments than object ones.

Keywords

Receiver Operational Characteristic Curve Perceptual Grouping Input Token Ambiguity Region Benchmark 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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leandro Loss
    • 1
  • George Bebis
    • 1
  • Mircea Nicolescu
    • 1
  • Alexei Skourikhine
    • 2
  1. 1.Computer Vision LaboratoryUniversity of NevadaReno
  2. 2.Space and Remote Sensing Sciences GroupLos Alamos National Laboratory 

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