Uncertain Geometry in Computer Vision

  • Peter Veelaert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3429)

Abstract

We give an overview of the main ideas and tools that have been employed in uncertain geometry. We show how several recognition problems in computer vision can be translated into combinatorial optimization problems that involve intersection hypergraphs, and how we can obtain approximate solutions for these problems when we replace the hypergraphs by intersection graphs. The statistical properties of these graphs are important when we design algorithms for the extraction of geometric primitives from images. We illustrate the use of uncertain geometry with examples involving the detection of circles and the computation of transformations between images.

Keywords

Feature Point Combinatorial Optimization Problem Image Point Maximum Clique Intersection Graph 
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 2005

Authors and Affiliations

  • Peter Veelaert
    • 1
  1. 1.Dept. INWEHogeschool GentBelgium

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