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Neural Networks and Robot Vision

  • Erik H. D’Hollander
Chapter
  • 118 Downloads
Part of the Microprocessor-Based Systems Engineering book series (ISCA, volume 6)

Abstract

Traditionally robot vision and robot control applications are written in imperative languages, such as Fortran, Pascal, Modula or C. Since the development of Artificial Intelligence techniques, specialized robot languages emerged. The upper layers of robot languages are: programmed in declarative languages, such as Lisp and Prolog. Recently, neural computing has become a third exciting programming alternative which complements the other programming styles. Neural programming paradigms apply to both robot vision and robot control. In this chapter the basic neural network techniques applicable to robot vision are explored. Among these are pattern recognition, blurred image restoration and unsupervised classification. It is likely that a judicious blend of these techniques will be used in complex scene analysis. Neural networks are characterized by an interconnection structure and a learning rule. These offer a wealth of combinations for each particular application. Therefore the major neural network models are analyzed and compared.

Keywords

Input Pattern Hide Unit Input Unit Linear Network Target Pattern 
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 Science+Business Media Dordrecht 1991

Authors and Affiliations

  • Erik H. D’Hollander
    • 1
  1. 1.State University of Ghent Department of Electrical EngineeringGhentBelgium

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