Acceleration of Canny Edge Detection Algorithm Using Parallel Clusters

International journal of new computer architectures and their applications Pub Date : 1900-01-01 DOI:10.17781/P002370

Njood S. Alassmi, S. S. Zaghloul

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引用次数: 2

Abstract

Image processing is a computational operation that requires many CPU cycles for simple image transformation. It takes every pixel of an image to perform a transformation to a new image. The image can be divided into smaller chunks, with same transformation operations being implemented on each. Thus, image processing is a good candidate for running on a parallel processor to improve the speed of computation when there are multiple images to be processed. In fact, this research focuses on Canny edge detection as a case study of probing parallelism. This work presents the design and implementation of sequential and parallel edge detection algorithms that are capable of producing high-quality results and performing at high speed. Therefore, this research aims to improve the Canny edge detection algorithm in terms of speed and scalability with different sizes of images. The algorithm is implemented using parallel clusters on KACST’s SANAM supercomputer. It is found that there is a valuable gained speedup with respect to the sequential version.In addition, it is found that more parallelism is explored in larger image sizes with Canny edge detector.

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基于并行聚类的Canny边缘检测算法加速

图像处理是一种计算操作，简单的图像转换需要很多CPU周期。它使用图像的每个像素来执行到新图像的转换。图像可以分成更小的块，在每个块上实现相同的转换操作。因此，当有多个图像需要处理时，图像处理是运行在并行处理器上以提高计算速度的一个很好的选择。事实上，本研究的重点是Canny边缘检测作为探测并行性的一个案例研究。这项工作提出了顺序和并行边缘检测算法的设计和实现，这些算法能够产生高质量的结果并以高速执行。因此，本研究旨在提高Canny边缘检测算法在不同大小图像下的速度和可扩展性。该算法在KACST的SANAM超级计算机上使用并行集群实现。结果发现，相对于顺序版本，获得了有价值的加速。此外，Canny边缘检测器在更大的图像尺寸下探索了更多的并行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International journal of new computer architectures and their applications

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