Mean Shift图像分割算法的并行化

图像分割作为高性能并行计算的一个主要应用领域，其算法本身的时间复杂度和实时性需求要求不断改进计算机硬件技术和并行处理的算法。Mean Shift算法是图像分割领域一个比较经典的算法，在图像分割过程中，不需要任何先验知识，是一种无监督的分割过程，在图像分割的具体实现中应用广泛。利用TBB（Threading Building Block）工具和CUDA（Compute Unified Device Architecture）对Mean Shift算法进行多核和GPU(Graphic Processing Unit )并行化改造。文中首先分析出Mean Shift分割过程中最耗时的部分Mean Shift聚类，之后利用TBB和CUDA对Mean Shift聚类进行了并行化改造，并对两种并行方法进行了对比分析。实验结果表明，两种并行方法都取得了较好的加速效果，加速比都随着图像增大和带宽参数的增加而增大，基于TBB的加速比稳定趋于核数。

Parallelization of Mean Shift image segmentation algorithm

Image segmentation is a main field of application in parallel computing.To achieve the high performance needed,the algorithm needs to make use of the improved computer hardware and parallel computing algorithms. Mean Shift algorithm is a relative classic algorithm in image segmentation field,which needs no prior knowledge and is an unsupervised segmentation process,attracting widespread attention for its good applicability. In this paper, we give two parallel improvement methods of Mean Shift using TBB(threading building block)and CUDA(compute unified device architecture)based on Multi-core and GPU(graphic processing unit) processing. First, the most time-consuming part the Mean Shift iteration in the process of Mean Shift image segmentation, is analyzed,then two parallel improvement methods of the Mean Shift iteration using TBB and CUDA are given, Two parallel methods are compared and analyzed. The experimental results show that,two kinds of parallel methods have achieved preferable acceleration effect,with the increase of the image and bandwidth parameter the speedup of two parallel methods is on the increases,and the speedup based on TBB tends to be equal to the number of CPUs.