GPU并行加速的均值偏移算法

Parallel Processing for Accelerated Mean Shift Algorithm with GPU

为克服mean shift算法计算复杂度高、运行速度慢的缺点,提出一种基于GPU的快速mean shift算法.首先使用k-means算法对图像像素进行预分类,之后在预分类、下采样后缩小的数据集上进行mean shift聚类,以有效地降低算法复杂度.此外,借助GPU的通用计算功能对k-means和mean shift分别进行并行了处理.实验结果表明,通过对图像进行预处理,有效地提高了几何模板查找在强噪声、低信噪比图像中的识别率;同时,改进后的mean shift算法的运行速度提高了近40倍,满足了高速机器视觉检测的实时性要求.

In order to overcome the shortcomings of the mean shift method for its intensive computational requirement, an improved GPU based mean shift algorithm is presented. By the novel algorithm, first k-means algorithm is used to pre-classify the source image with a re-sampling, then mean shift runs on the narrowed re-sampled data sets. As a result, ,the algorithm complexity can be effectively reduced. In addition, through the further study of k-means and mean shift, and with general purpose computation of GPU, k-means and mean shift are respectively parallel processed. Experimental results show that by preprocessing the images, the accurate recognition rate of Geometric Model Finder for intensive noises, low SNR images is effectively improved. At the same time, the efficiency of the modified mean shift algorithm is greatly improved, with the average processing time nearly 40 times faster.. It meets the requirement of high speed machine vision inspection.