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A programmable VLSI filter architecture for application in real-time vision processing systems.

T Serrano-Gotarredona1, A G Andreou, B Linares-Barranco

  • 1Instituto de Microelectrónica de Sevilla, Centro Nacional de Microelectrónica, Spain. terese@imse.cnm.es

International Journal of Neural Systems
|September 30, 2000
PubMed
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A novel architecture enables real-time edge-extraction filtering in Address-Event-Representation (AER) vision systems. This approach efficiently performs 2D filtering operations by decomposing kernels, suitable for advanced vision models.

Area of Science:

  • Computer Vision
  • Neuromorphic Engineering
  • VLSI Design

Background:

  • Address-Event-Representation (AER) vision systems offer event-driven processing.
  • Real-time edge extraction is crucial for many computer vision tasks.
  • Existing methods may struggle with arbitrary kernel rotations and real-time constraints.

Purpose of the Study:

  • To propose a novel architecture for real-time edge-extraction filtering in AER vision systems.
  • To demonstrate the architecture's applicability to any 2D filtering operation with separable convolutional kernels.
  • To validate the architecture's performance and circuit-level implementation.

Main Methods:

  • Developed an architecture leveraging AER for real-time address manipulation.
  • Utilized kernel decomposition (F(p,q)=H(p)V(q)) in a rotated coordinate system.

Related Experiment Videos

  • Approximated the product operation with a signed minimum operation for efficiency.
  • Implemented critical subcircuits using MOS transistors in weak inversion.
  • Conducted system-level behavioral simulations and circuit-level electrical simulations/experiments.

    • Main Results:

    • The proposed architecture successfully performs real-time edge-extraction filtering.
    • The approach is generalizable to any 2D filtering operation with separable kernels.
    • The signed minimum approximation shows no significant performance degradation for edge extraction.
    • Circuit-level simulations and experiments validate the feasibility of critical subcircuits.

    Conclusions:

    • The proposed architecture provides an efficient solution for real-time edge-extraction in AER vision systems.
    • The design is adaptable for various filtering operations and kernel orientations.
    • This work contributes to the development of advanced vision models like the Boundary-Contour-System and Feature-Contour-System Vision Model.