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A novel approach of adaptive thresholding for image segmentation on GPU

机译:一种在GPU上进行图像分割的自适应阈值处理新方法

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摘要

In the recent decades, graphical processing unit (GPU) improvises the programmability in the area of image processing for reducing the computational time as compare to CPU. GPUs can solve large data parallel problems and tends to give most robust and energy efficient segmentation method. It also gives the concept of memory pre-allocation and vectorization and handled the large image data set very profoundly for accelerated image segmentation method. In this paper, investigation results are obtained on 100 gold standard dermoscopy images which reveal that the proposed novel method of image segmentation is benefited from GPU processing in terms of speed and accuracy for lesion detection.
机译:在最近的几十年中,图形处理单元(GPU)改善了图像处理领域的可编程性,与CPU相比减少了计算时间。 GPU可以解决大数据并行问题,并倾向于提供最健壮和最节能的分割方法。它还给出了内存预分配和矢量化的概念,并针对加速图像分割方法非常深刻地处理了大图像数据集。在本文中,对100张金标准皮肤镜检查图像获得的调查结果表明,该新的图像分割方法在病变检测的速度和准确性方面受益于GPU处理。

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