Initial Results on the Effectiveness of Distributed Edge Detection for Large Image Data Sets
Initial Results on the Effectiveness of Distributed Edge Detection for Large Image Data Sets
复制标题
大型图像数据集分布式边缘检测有效性的初步结果
DOI：
10.1109/southeastcon48659.2022.9763960
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Mocanu Mihai
中科院分区：
文献类型：
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作者：
Ivanescu Constantin Renato;Mocanu Mihai
随着技术的发展，图像数据正在迅速扩展，因此必须考虑高效的解决方案，以便在处理大型图像数据集的情况下实现高、实时的性能。当使用现有的分布式体系结构时，并行处理越来越多地被用作提高性能的一种有吸引力的替代方案，也用于顺序商品计算机。它可以提供加速、效率、可靠性、增量增长和灵活性。我们提出了这样一种替代方案，并强调了与单个CPU相比，在小型pc集群上加速计算的方法的有效性。边缘检测是图像处理和计算机视觉领域的一项基础且具有挑战性的任务，本文的重点是在大图像数据集上应用边缘检测。五种不同的技术，主要是Sobel， Prewitt, LoG， Canny和Roberts，在一个简单的实验设置中进行比较，其中包括用于图像像素处理的OpenCV库函数。利用高斯模糊来减少高频分量，以控制边缘检测受到的噪声影响。总的来说，这项工作是对大型图像数据集的图像分割方法进行更广泛研究的一部分，但所提出的结果是相关的，并显示了我们方法的有效性。
Image data is expanding rapidly, along with technology development, so efficient solutions must be considered to achieve high, real-time performance in the case of processing large image datasets. Parallel processing is increasingly used as an attractive alternative to improve the performance, when using existing distributed architectures but also for sequential commodity computers. It can provide speedup, efficiency, reliability, incremental growth, and flexibility. We present such an alternative and stress the effectiveness of the methods to accelerate computations on a small cluster of PCs compared to a single CPU. Our paper is focused on applying edge detection on large image data sets, as a fundamental and challenging task in image processing and computer vision. Five different techniques, mainly Sobel, Prewitt, LoG, Canny, and Roberts, are compared in a simple experimental setup that includes the OpenCV library functions for image pixels manipulation. Gaussian blur is used to reduce high-frequency components to manage the noise that edge detection is impacted by. Overall, this work is part of a more extensive investigation of image segmentation methods on large image datasets, but the results presented are relevant and show the effectiveness of our approach.