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Probabilistic Decisions in Production Net An Example from Vehicle Recognition

  • Eckart Michaelsen
  • Uwe Stilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

A structural knowledge-based vehicle recognition method is modified yielding a new probabilistic foundation for the decisions. The method uses a pre-calculated set of hidden line projected views of articulated polyhedral models of the vehicles. Model view structures are set into correspondence with structures composed from edge lines in the image. The correspondence space is searched utilizing a 4D Hough-type accumulator. Probabilistic models of the background and the error in the measurements of the image structures lead to likelihood estimations that are used for the decision. The likelihood is propagated along the structure of the articulated model. The system is tested on a cluttered outdoor scene. To ensure any-time performance the recognition process is implemented in a data-driven production system.

Keywords

Search Area Probabilistic Decision Line Object Generalize Hough Transformation Correspondence Space 
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 2002

Authors and Affiliations

  • Eckart Michaelsen
    • 1
  • Uwe Stilla
    • 1
  1. 1.FGAN-FOM Research Institute for Optronics and Pattern RecognitionEttlingenGermany

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