IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i12p371-d1000200.html
   My bibliography  Save this article

Implementation of the Canny Edge Detector Using a Spiking Neural Network

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/12/371/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/12/371/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Toshihiko Hosoya & Stephen A. Baccus & Markus Meister, 2005. "Dynamic predictive coding by the retina," Nature, Nature, vol. 436(7047), pages 71-77, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias S Keil & Agata Lapedriza & David Masip & Jordi Vitria, 2008. "Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-5, July.
    2. Matthias S Keil, 2009. "“I Look in Your Eyes, Honey”: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.
    3. Tristan G. Heintz & Antonio J. Hinojosa & Sina E. Dominiak & Leon Lagnado, 2022. "Opposite forms of adaptation in mouse visual cortex are controlled by distinct inhibitory microcircuits," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Miguel Maravall & Rasmus S Petersen & Adrienne L Fairhall & Ehsan Arabzadeh & Mathew E Diamond, 2007. "Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex," PLOS Biology, Public Library of Science, vol. 5(2), pages 1-12, January.
    5. Marcus H C Howlett & Robert G Smith & Maarten Kamermans, 2017. "A novel mechanism of cone photoreceptor adaptation," PLOS Biology, Public Library of Science, vol. 15(4), pages 1-28, April.
    6. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.
    7. Gabriel D Puccini & Maria V Sanchez-Vives & Albert Compte, 2007. "Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits," PLOS Computational Biology, Public Library of Science, vol. 3(5), pages 1-13, May.
    8. Johnatan Aljadeff & Ronen Segev & Michael J Berry II & Tatyana O Sharpee, 2013. "Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-12, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:371-:d:1000200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.