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Memory Networks for Practical Vision Systems: Design Calculations

  • I. Aleksander

Abstract

If memory networks are to be used in real robotic applications, the designer needs some basis from which to calculate the size of the modules required and the amount of training that provides optimal results. This paper is concerned with the recognition of solid shapes, and provides design calculations and methods for systems that recognize objects irrespective of their location or orientation. It tackles the problem of the recognition of partially hidden or overlapping objects, and the measurement of position and orientation itself.

Keywords

Finite State Machine Random Access Memory Image Recognition Training Pattern Artificial Vision 
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

© Igor Aleksander 1983

Authors and Affiliations

  • I. Aleksander
    • 1
  1. 1.Department of Electrical Engineering and ElectronicsBrunel UniversityEngland

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