摘要
不变矩自提出以来被广泛应用于目标识别系统中进行特征描述,这需要能够实时计算不变矩值。虽然已经提出了许多不变矩的快速算法,但仍无法在单台PC机上实现不变矩的实时计算。分析了基于差分矩因子的不变矩快速算法的并行性,提出了一种基于统一计算架构(CUDA)的快速不变矩并行实现方法,并在NVIDIA Tesla C1060 GPU上实现。对所提出算法的计算性能与普通串行算法进行了对比分析。实验结果表明,所提出的并行计算方法极大地提高了不变矩的计算速度,可有效地用来进行实时特征提取。
Moment invariants have been used as feature descriptors in a variety of object recognition applications since it was proposed.It is necessary to compute geometric moment values in real-time rate.Despite the existence of many algorithms of fast computation of moments,it cannot be implemented for real-time computation to be run on a PC.After analyzing the parallelism of fast moment invariants algorithm based on differential of moments factor,a new parallel computing method based on CUDA（Compute Unified Device Architecture） technology was presented and implemented on NVIDIA Tesla C1060 GPU（Graphic Processing Unit） in this paper.The computation performance of the proposed method and the traditional serial algorithm was contrasted and analyzed.The experiments show that the parallel algorithm presented in the paper greatly improves the speed of the computation of moments.The new method can be effectively used in real-time feature extraction.
出处
《计算机应用》
CSCD
北大核心
2010年第7期1983-1986,共4页
journal of Computer Applications
关键词
不变矩
并行计算
统一计算架构
协同计算
moment invariant
parallel computing
Compute Unified Device Architecture（CUDA）
cooperative computing
