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Evidence Fusion Using Constraint Satisfaction Networks

  • Conference paper
Multisensor Fusion for Computer Vision

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

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Abstract

This paper describes a modular and coherent approach to 3D object recognition. The basic theme behind the approach is constraint satisfaction networks, motivated by connectionist networks. The use of such networks allows fusion of evidence and a fundamental homogeneity of the entire recognition paradigm. Here evidence can be about fundamentally different geometric objects, such as curves and surfaces, or can be evidence obtained from different sensory modalities. We emphasize the former.

In particular, we discuss the problems that arise when dealing with large object databases and complex input scenery. We propose solutions that will keep the size of the networks from growing beyond control.

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Author information

Authors and Affiliations

  1. Exploratory Computer Vision Group, IBM Thomas J. Watson Research Center, USA

    Andrea Califano, Ruud M. Bolle, Rick Kjeldsen & Russell W. Taylor

Authors
  1. Andrea Califano
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  2. Ruud M. Bolle
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  3. Rick Kjeldsen
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  4. Russell W. Taylor
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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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Califano, A., Bolle, R.M., Kjeldsen, R., Taylor, R.W. (1993). Evidence Fusion Using Constraint Satisfaction Networks. 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_14

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  • DOI: https://doi.org/10.1007/978-3-662-02957-2_14

  • 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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