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Scale-space ridge detection with GPU acceleration

M. Kinsner, D. Capson, A. Spence
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, ON
Canadian Conference on Electrical and Computer Engineering, 2008. CCECE 2008

@inproceedings{kinsner2008scale,

   title={Scale-space ridge detection with GPU acceleration},

   author={Kinsner, M. and Capson, D. and Spence, A.},

   booktitle={Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on},

   pages={001527–001530},

   organization={IEEE},

   year={2008}

}

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Imaging systems for computer vision play an important role in today’s world. Typical computer vision systems operate on large scale scenes, where objects are relatively far from the camera and the depth of field in which objects appear focussed is large. Close-range camera systems, on the other hand, typically have a narrow depth of field. World features outside this depth of field are blurred, and in applications where poor data may not be re-acquired, a technique is required to reliably extract information from these images. Discrete scale-space feature detection techniques provide methods to extract features from these images, but bring with them a significantly higher computational workload compared with classical edge and ridge detectors. This paper presents the results from implementation of a discrete scale-space ridge detector with graphics processing unit (GPU) acceleration. This feature detector has been applied to close-range images of grids printed on sheet metal surfaces, and a speedup of one to two orders of magnitude is seen over a CPU-based implementation of the same feature detector.
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