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Implementation of the Canny Edge Detector Using a Spiking Neural Network

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  • Krishnamurthy V. Vemuru

    (Riverside Research, 2900 Crystal Dr., Arlington, VA 22202, USA
    Current Address: BrainChip Inc., 23041 Avenida de la Carlota, Laguna Hills, CA 92653, USA.)

Abstract

Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step of noise reduction using a Gaussian kernel and a final step to remove the weak edges by the hysteresis threshold. In this work, a spike-based computing algorithm is presented as a neuromorphic analogue of the Canny edge detector, where the five steps of the conventional algorithm are processed using spikes. A spiking neural network layer consisting of a simplified version of a conductance-based Hodgkin–Huxley neuron as a building block is used to calculate the gradients. The effectiveness of the spiking neural-network-based algorithm is demonstrated on a variety of images, showing its successful adaptation of the principle of the Canny edge detector. These results demonstrate that the proposed algorithm performs as a complete spike domain implementation of the Canny edge detector.

Suggested Citation

  • Krishnamurthy V. Vemuru, 2022. "Implementation of the Canny Edge Detector Using a Spiking Neural Network," Future Internet, MDPI, vol. 14(12), pages 1-12, December.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:371-:d:1000200
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    References listed on IDEAS

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    1. Toshihiko Hosoya & Stephen A. Baccus & Markus Meister, 2005. "Dynamic predictive coding by the retina," Nature, Nature, vol. 436(7047), pages 71-77, July.
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