针对SIFT、SURF等局部不变特征在大尺寸图像上匹配时过于耗时的问题,将FREAK算子应用于图像匹配中,并提出一种多线程并行加速方法.首先介绍FREAK描述子的特征点的检测、特征描述向量的生成和特征向量的匹配的过程,并分析其优势.其次提出并行处理的2种思路:一是对待匹配图像进行有重叠的分块,对于每一块子图像,开辟新的线程分别进行处理;二是对匹配过程的3个步骤,采用流水线技术进行并行处理,每检测出一个特征点,随即提取出该点的特征向量,然后和模板图像的特征向量集进行匹配.改写SIFT、SURF和FREAK算法进行实验验证,结果证明FREAK计算过程比SIFT和SURF快得多,而并行方法可以在保证匹配精度的同时明显缩短匹配时间.
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