针对传统图像拼接算法速度较慢, 难以满足获取大分辨率全景图像的实时性要求, 本文提出一种基于CUDA的快速鲁棒特征(speeded-up-robust features, SURF)图像配准算法, 从GPU线程执行模型、编程模型和内存模型等方面, 对传统SURF算法特征点的检测和描述进行CUDA并行优化; 基于FLANN和RANSAC算法, 采用双向匹配策略进行特征匹配, 提高配准精度. 结果表明, 相对串行算法, 本文并行算法对不同分辨率的图像均可实现10倍以上的加速比, 而且配准精度较传统配准算法提高17%, 精度最优可高达96%. 基于CUDA加速的SURF算法可广泛应用于安防监控领域, 实现全景图像的实时配准.
Traditional image stitching algorithms are slow and fail to meet the requirements of obtaining large-resolution panoramic images in real time. To solve these problems, this study proposes an image registration algorithm based on CUDA’s speeded-up-robust features (SURF) and carries out CUDA parallel optimization on the detection and description of feature points of traditional SURF algorithms in terms of GPU thread execution model, programming model, and memory model. In addition, based on FLANN and RANSAC algorithms, the study adopts a bidirectional matching strategy to match features and improve registration accuracy. The experimental results show that compared with serial algorithms, the proposed parallel algorithm can achieve an acceleration ratio of more than 10 times for images with different resolutions, and the registration accuracy is 17% higher than that of traditional registration algorithms, with an optimal accuracy of as high as 96%. Therefore, the SURF algorithm based on CUDA acceleration can be widely used in the field of security monitoring to realize the real-time registration of panoramic images.