基于FAST和BRIEF的图像匹配算法

摘要

To solve the problem of low matching rate and poor efficiency of usual image matching algorithm,improved FAST (features from accelerated segment test)and BRIEF (binary robust independent elementary features)were adopted to match images.Firstly,FAST algorithm was used to extract keypoints from images,and test model was simplified to improve detection speed.Secondly,the patch orientation of keypoints was computed using intensity centroid method,and rotation invariance was guaranteed by steering BRIEF descriptors according to the orientation of keypoints.Finally,the keypoints descriptor similarity was evaluated using the Hamming distance,which was very efficient to compute and used to features matching.By comparing with others algorithms,the experimental results show that the algorithm reduces computing complexity while maintaining a rela-tively good matching rate.%针对图像匹配算法中匹配率不高以及运算速度较慢等问题，采用改进的FAST （加速分割测试特征）和BRIEF （二进制稳健基元独立特征）算法对图像进行匹配。使用FAST算法提取图像特征点，简化测试模板以提高检测速度；以提取的特征点为中心，使用强度质心方法计算图像块的主方向，根据主方向旋转BRIEF描述器，使其具备旋转不变性；使用易于计算的汉明距离度量各描述器的相似度，据此进行匹配特征。通过和其余算法进行对比实验，验证了该算法在保持高匹配率的同时，降低了计算复杂性。