DMCA
Canny edge detection on nvidia cuda (2008)
Cached
Download Links
| Venue: | Computer Vision and Pattern Recognition Workshops |
| Citations: | 11 - 0 self |
BibTeX
@INPROCEEDINGS{Luo08cannyedge,
author = {Yuancheng “mike Luo and Ramani Duraiswami},
title = {Canny edge detection on nvidia cuda},
booktitle = {Computer Vision and Pattern Recognition Workshops},
year = {2008},
pages = {1--8}
}
Share
 |
 |
 |
 |
||
OpenURL
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 “true ” edges, while suppressing “false ” 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 MAT-LAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source. 1.
Keyphrases
canny edge detection nvidia cuda programmer friendly cuda framework source code old style gpgpu multi-step detector open source true edge many computer vision algorithm edge detector pre-processing step popular interactive simulation package graphical application layer connected-component analysis stage false non edge filter response entire canny algorithm non-maxima suppression canny edge detector cpu implementation effective edge feature detector
