Open Access
Image Convolution with CUDA
Victor Podlozhnyuk
- 01 Jan 2007
TL;DR: This document shows how a separable convolution filter can be implemented in NVIDIA CUDA and provides some guidelines for performance optimizations.
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Abstract: Convolution filtering is a technique that can be used for a wide array of image processing tasks, some of which may include smoothing and edge detection. In this document we show how a separable convolution filter can be implemented in NVIDIA CUDA and provide some guidelines for performance optimizations.
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Citations
Medical Image Processing on the GPU : Past, Present and Future
TL;DR: This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations.
426
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
Chris Gregg,Kim Hazelwood +1 more
- 10 Apr 2011
TL;DR: A taxonomy for future CPU/GPU comparisons is suggested, and it is argued that this is not only germane for reporting performance, but is important to heterogeneous scheduling research in general.
350
Medical image segmentation on GPUs – A comprehensive review
Erik Smistad,Erik Smistad,Thomas L. Falch,Mohammadmehdi Bozorgi,Anne C. Elster,Frank Lindseth,Frank Lindseth +6 more
TL;DR: It is concluded that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count, however, factors such as synchronization, branch divergence and memory usage can limit the speedup.
287
Canny edge detection on NVIDIA CUDA
Yuancheng Luo,Ramani Duraiswami +1 more
- 23 Jun 2008
TL;DR: This work implements the entire Canny algorithm on GPUs using the more programmer friendly CUDA framework, and integrates the detector in to MATLAB, a popular interactive simulation package often used by researchers.
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