Real-time gradient vector flow on GPUs using OpenCL

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• 作者：Erik Smistad (1)
  Anne C. Elster (1)
  Frank Lindseth (1) (2)
  
  1. Department of Computer and Information Science ; Norwegian University of Science and Technology ; Sem Saelandsvei 7-9 ; 7491 ; Trondheim ; Norway
  2. SINTEF Medical Technology ; Trondheim ; Norway
  
• 关键词：Gradient Vector Flow ; GPU ; OpenCL
• 刊名：Journal of Real-Time Image Processing
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：10
• 期：1
• 页码：67-74
• 全文大小：1,337 KB
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  9. NVIDIA: OpenCL best practices guide. technical report, 2009. http://www.nvidia.com/content/cudazone/CUDABrowser/downloads/papers/NVIDIA_OpenCL_BestPracticesGuide.pdf. Last accessed December 2011
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• 刊物类别：Computer Science
• 刊物主题：Image Processing and Computer Vision
  Multimedia Information Systems
  Computer Graphics
  Pattern Recognition
  Signal,Image and Speech Processing
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1861-8219

文摘

The Gradient Vector Flow (GVF) is a feature-preserving spatial diffusion of gradients. It is used extensively in several image segmentation and skeletonization algorithms. Calculating the GVF is slow as many iterations are needed to reach convergence. However, each pixel or voxel can be processed in parallel for each iteration. This makes GVF ideal for execution on Graphic Processing Units (GPUs). In this paper, we present a highly optimized parallel GPU implementation of GVF written in OpenCL. We have investigated memory access optimization for GPUs, such as using texture memory, shared memory and a compressed storage format. Our results show that this algorithm really benefits from using the texture memory and the compressed storage format on the GPU. Shared memory, on the other hand, makes the calculations slower with or without the other optimizations because of an increased kernel complexity and synchronization. With these optimizations our implementation can process 2D images of large sizes (5122) in real-time and 3D images (2563) using only a few seconds on modern GPUs.