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International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

EMMCVPR 1997: Energy Minimization Methods in Computer Vision and Pattern Recognition pp 311–326Cite as

Optimization problems in statistical object recognition

  • Object Recognition
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1223))

Abstract

This paper treats the application of statistical principles for 3D computer vision purposes. Both the automatic generation of probabilistic object models, and the localization as well as the classification of objects in compound scenes result in complex optimization problems within the introduced statistical framework. Different methods are discussed for solving the associated optimization problems: the Expectation-Maximization algorithm forms the basis for the learning stage of stochastic object models; global optimization techniques—like adaptive random search, deterministic grid search or simulated annealing—are used for localization. The experimental part utilizes the abstract formalism for normally distributed object features, proves the correctness of 3D object recognition algorithms, and demonstrates their computational complexity in combination with real gray-level images.

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Author information

Authors and Affiliations

  1. Lehrstuhl für Mustererkennung (Informatik 5), Martensstra\e 3, D-91058, Erlangen, Germany

    Joachim Hornegger & Heinrich Niemann

Authors
  1. Joachim Hornegger
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  2. Heinrich Niemann
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Editor information

Marcello Pelillo Edwin R. Hancock

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© 1997 Springer-Verlag Berlin Heidelberg

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Hornegger, J., Niemann, H. (1997). Optimization problems in statistical object recognition. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_88

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  • DOI: https://doi.org/10.1007/3-540-62909-2_88

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62909-2

  • Online ISBN: 978-3-540-69042-9

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