【摘要】 加速鲁棒特征算法(speed up robust features,SURF)的时间复杂度大,传统串行计算的方法,实时性难以保证.针对上述问题,提出一种基于OpenCL架构的SURF并行实现方法.首先对算法中的积分图的计算、Hessian响应图、特征点主方向、特征点描述等步骤实施数据并行和任务并行处理,并给出详细的算法流程.接着从OpenCL架构的数据传输、内存访问以及负载均衡等方面优化算法性能.实验结果表明,该算法对不同分辨率的图片均实现了10倍以上的加速比,一些高分辨率的图片甚至可以达到39.5倍,并且算法适用于多种通用计算平台.更多还原

【Abstract】 SURF algorithm has high computational complexity and can not meet the real-time requirement.To solve these problems,aparallel SURF algorithm based on OpenCL was presented.Firstly,data parallelism and task parallelism model were applied to the calculations of the integral images,Hessian detector,orientation and descriptor,and the detailed algorithm process was given.Secondly,the performance of the parallel algorithm was optimized from data transmission,memory access and load balancing.The experimental results show that the algorithm can achieve more than 10 times speedup for images with different resolution,and some high-resolution images can even reach up to 39.5 times.Furthermore,it can be applied to a variety of general purpose computing platforms.更多还原