基于ORB特征的图像误匹配剔除方法研究

针对图像特征匹配当中存在明显错误匹配、匹配准确度较差的问题,对ORB算法和RANSAC算法进行了研究。ORB算法中,特征点的匹配是基于汉明距离进行的。在此前提下,提出了一种基于RANSAC算法的改进算法进行误匹配剔除。该算法通过增加粗剔除过程来剔除一部分错误匹配,然后利用RANSAC算法做了进一步剔除,同时增加了RANSAC算法中的初始样本集的数量并加以预判断,达到了降低RANSAC算法耗时的目的。最后利用多组图像对该算法进行了验证,实验结果表明,该算法可以有效剔除误匹配,提高图像特征匹配的准确度,并且具有旋转不变特性和噪声抑制特性,同时也保证了ORB算法的匹配速度。

Remove approach of wrongly matched image based on ORB features

Aiming at the problems of many wrong matches and poor matching accuracy in feature-based image matching,ORB (oriented brief) and RASNAC (RANdom sample consensus) algorithm were researched.On condition that matching was achieved based on hamming distance in ORB algorithm,a method based on improved RANSAC was proposed.After matching based on hamming distance,the pre-removing process was added to eliminate a part of wrong matches.Then,further elimination was achieved through RANSAC algorithm.Meanwhile,the computing time was saved by increasing the amount of initial sample and pre-estimating the sample.At last several images were used to verify the validity.The experimental results indicate that the method can eliminate most wrong matches to improve the matching accuracy,and can suppress the effects of noise.It's also invariant about rotation.Meanwhile,it guarantees the computation speed.