• DocumentCode
    2118735
  • Title

    Canny edge detection on NVIDIA CUDA

  • Author

    Luo, Yuancheng Mike ; Duraiswami, Ramani

  • Author_Institution
    Perceptual Interfaces & Reality Lab., Maryland, Univ., College Park, MD
  • fYear
    2008
  • fDate
    23-28 June 2008
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The Canny edge detector is a very popular and effective edge feature detector that is used as a pre-processing step in many computer vision algorithms. It is a multi-step detector which performs smoothing and filtering, non-maxima suppression, followed by a connected-component analysis stage to detect ldquotruerdquo edges, while suppressing ldquofalserdquo non edge filter responses. While there have been previous (partial) implementations of the Canny and other edge detectors on GPUs, they have been focussed on the old style GPGPU computing with programming using graphical application layers. Using the more programmer friendly CUDA framework, we are able to implement the entire Canny algorithm. Details are presented along with a comparison with CPU implementations. We also integrate our detector in to MATLAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source.
  • Keywords
    computer vision; edge detection; feature extraction; smoothing methods; Canny edge detection; NVIDIA CUDA; computer vision algorithms; connected-component analysis stage; edge feature detector; filtering; graphical application layers; multistep detector; non edge filter responses; nonmaxima suppression; smoothing; Computational modeling; Computer vision; Detectors; Filtering; Filters; Image edge detection; MATLAB; Performance analysis; Programming profession; Smoothing methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    2160-7508
  • Print_ISBN
    978-1-4244-2339-2
  • Electronic_ISBN
    2160-7508
  • Type

    conf

  • DOI
    10.1109/CVPRW.2008.4563088
  • Filename
    4563088