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Neural network feature detector for real-time video signal processing

D Naylor1, S Jones, D Myers

  • 1Department of Electronic and Electrical Engineering, Loughborough University of Technology, Leicestershire, England.

International Journal of Neural Systems
|December 1, 1993
PubMed
Summary
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A new hardware system integrates the HANNIBAL linear array processor for real-time artificial neural network image processing. This flexible, high-performance tool enables advanced feature recognition and neural image application studies.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Hardware Engineering

Background:

  • Real-time image processing using artificial neural networks (ANNs) demands specialized, high-performance hardware.
  • Existing systems often lack the dedicated architecture for efficient ANN implementation.

Purpose of the Study:

  • To design a comprehensive neural system integrating the HANNIBAL linear array processor.
  • To create a flexible, high-performance hardware tool for evaluating various neural image processing applications.
  • To facilitate feature recognition tasks through optimized ANN hardware.

Main Methods:

  • Development of HANNIBAL, a linear array processor implementing the backpropagation neural learning algorithm on-chip.
  • Integration of HANNIBAL into an existing image processing environment.

Related Experiment Videos

  • System design focused on flexibility and high performance for neural applications.

    • Main Results:

    • Successful design of a complete neural system incorporating HANNIBAL.
    • The system provides a high-performance platform for neural image processing.
    • Demonstrated capability for feature recognition tasks.

    Conclusions:

    • The integrated HANNIBAL system offers a powerful solution for real-time ANNs in image processing.
    • This hardware facilitates research and development in neural image analysis.
    • The system's flexibility supports a wide range of future neural network applications.