OpenCV 4 with Python Blueprints - Second Edition

Testing the ratio for outlier removal

The more correct matches that are found (which means that more pattern-to-image correspondences exist), the higher the chance that the pattern is present in the image. However, some matches might be false positives.

A well-known technique for removing outliers is called the ratio test. Since we performed kNN-matching with k=2, the two nearest descriptors are returned for each match. The first match is the closest neighbor and the second match is the second-closest neighbor. Intuitively, a correct match will have a much closer first neighbor than its second-closest neighbor. On the other hand, the two closest neighbors will be at a similar distance from an incorrect match.

Therefore, we can find out how ...