Statistical Classifiers in Computer Vision

  • J. Hornegger
  • D. Paulus
  • H. Niemann
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This paper introduces a unified Bayesian approach to 3-D computer vision using segmented image features. The theoretical part summarizes the basic requirements of statistical object recognition systems. Non-standard types of models are introduced using parametric probability density functions, which allow the implementation of Bayesian classifiers for object recognition purposes. The importance of model densities is demonstrated by concrete examples. Normally distributed features are used for automatic learning, localization, and classification. The contribution concludes with the experimental evaluation of the presented theoretical approach.

Keywords

Covariance 

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • J. Hornegger
    • 1
  • D. Paulus
    • 1
  • H. Niemann
    • 1
  1. 1.Lehrstuhl für Mustererkennung (Informatik 5)Universität ErlangenErlangenGermany

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