关键词: 图形处理器（GPU）, 多核CPU, 高斯差分（DoG）, 特征点检测, 并行算法

Abstract:

Feature point detection is widely used in target recognition, tracking and 3D reconstruction. The computation of the feature point detection algorithm for big data problem is time-consuming and computation-intensive. In this paper, the parallel DoG（Difference-of-Gaussian） feature point detection algorithms are proposed. In the multi-CPU programming model based on OpenMP and the GPU parallel environment based on CUDA and OpenCL architecture, the parallel algorithms of the DoG feature point detection algorithm are designed and implemented. The comparison experiment of hallFeng image set is completed on different platforms, the experimental results show that the multi-CPU feature point detection algorithm based on OpenMP shows good multi-core scalability. The parallel GPU algorithm based on CUDA and OpenCL architecture can achieve the high speedup ratio, up to 96.79, with significant speedup effect, and which has good data and platform scalability.

Key words: Graphics Processing Unit（GPU）, Multi-CPU, Difference-of-Gaussian（DoG）, feature point detection, parallel algorithm