Multipurification of matching pairs based on ORB feature and PCB alignment case study

Abstract

To address the scale invariance and the mismatching problems of ORB (oriented FAST and rotated BRIEF), an improved algorithm based on multipurification was put forward and applied in PCB matching and positioning. ORB feature points were initially extracted from both the template image and the target image. Rough matching was then performed by the combination of k-nearest neighbor (k-NN) algorithm and best bin first search algorithm. Considering the existence of a large number of mismatches, the multipurification that consists of neighborhood ratio, bidirectional matching, and cosine similarity was used to purify the set of matching pairs. Finally, the affine matrix between the two images was solved by progressive sample consensus. The experimental results showed that the matching accuracy was significantly improved after the purification by the proposed method. The improved algorithm outperformed original ORB under the condition of image rotation and scaling. The mean rotation angle error was just 1/10th of that of original ORB. The time overhead of the improved algorithm was comparable to that of original ORB, which was about six times faster than that of speeded up robust features and 30 times faster than that of scale-invariant feature transform. The enhanced algorithm demonstrated great advantages in the improvement of accuracy, time overhead, and robustness for the PCB alignment.