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A Bayesian multiple hypothesis approach to contour grouping

  • Ingemar J. Cox
  • James M. Rehg
  • Sunita Hingorani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 588)

Abstract

We present an approach to contour grouping based on classical tracking techniques. Edge points are segmented into smooth curves so as to minimize a recursively updated Bayesian probability measure. The resulting algorithm employs local smoothness constraints and a statistical description of edge detection, and can accurately handle corners, bifurcations, and curve intersections. Experimental results demonstrate good performance.

Keywords

False Alarm Edge Point Surveillance Region Parent Hypothesis Global Hypothesis 
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 1992

Authors and Affiliations

  • Ingemar J. Cox
    • 1
  • James M. Rehg
    • 2
  • Sunita Hingorani
    • 1
  1. 1.NEC Research InstitutePrinceton
  2. 2.Dept. of Electrical and Computer Eng.Carnegie Mellon UniversityPittsburgh

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