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A new approach for parallel region growing algorithm in image segmentation using MATLAB on GPU architecture

机译:用MATLAB对GPU架构的平行区域生长算法的一种新方法

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Image segmentation is the process of dividing a digital image into multiple segments or clusters. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful for analysis. Image segmentation is typically used to separate the region of interest from the input image. So we have different region of interest for different applications. None of the algorithm will satisfy this global application to give better result from the computational point of view. So Segmentation is one of the challenging issues in Digital Image Processing. Many general-purpose algorithms have been developed for image segmentation in which Region Growing is one of them. This paper presents a comparison between serial execution of the Region growing algorithm and Parallel execution of it on CUDA platform (provided by nvidia) integration with MATLAB. Finally we are able to solve the computational complexity and improve the real-time performance of Region Growing using GPU computing. MATLAB is used as a platform for serial execution and CUDA is used as a platform for parallel execution.
机译:图像分割是将数字图像划分为多个段或群集的过程。分割的目标是简化和/或将图像的表示更改为对分析更加有意义的东西。图像分割通常用于将感兴趣区域与输入图像分开。因此,我们对不同的应用有不同的兴趣区域。算法都不满足这个全局应用程序,以提供从计算的观点来提供更好的结果。因此,分割是数字图像处理的具有挑战性问题之一。已经开发了许多通用算法用于图像分割,其中区域生长是其中之一。本文展示了该地区越来越多的算法与CUDA平台(由NVIDIA提供)与MATLAB集成的平行执行之间的比较。最后,我们能够解决计算复杂性并通过GPU计算改善区域生长的实时性能。 MATLAB用作串行执行的平台,CUDA用作并行执行的平台。

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