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Information Integration and Model Selection in Computer Vision

  • Conference paper
Multisensor Fusion for Computer Vision

Part of the book series: NATO ASI Series ((NATO ASI F,volume 99))

  • 155 Accesses

Abstract

An information processing system using sensor data to derive properties of the external world typically uses precise mathematical and physical models to do so. A crucial point then is to select the appropriate model or sensor to base the computations on. In realistic cases it is generally difficult or infeasible to control the environment. On the other hand, different situations as well as different sensors assume different models. Hence, one is in general faced with a difficult model estimation problem, especially in the presence of noise and conflicting observations.

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Author information

Authors and Affiliations

  1. Computational Vision and Active Perception Laboratory, Royal Institute of Technology, S-100 44, Stockholm, Sweden

    Jan-Olof Eklundh

Authors
  1. Jan-Olof Eklundh
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Editor information

Editors and Affiliations

  1. Computer and Vision Research Center Department of Electrical and Computer Engineering, The University of Texas at Austin, 78712-1084, Austin, TX, USA

    J. K. Aggarwal (Director) (Director)

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© 1993 Springer-Verlag Berlin Heidelberg

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Eklundh, JO. (1993). Information Integration and Model Selection in Computer Vision. In: Aggarwal, J.K. (eds) Multisensor Fusion for Computer Vision. NATO ASI Series, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02957-2_1

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  • DOI: https://doi.org/10.1007/978-3-662-02957-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08135-4

  • Online ISBN: 978-3-662-02957-2

  • eBook Packages: Springer Book Archive

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