Relative orientation and scale for improved feature matching

Despite recent attention paid to feature detection and description methods, the basic criterion for feature matching has remained largely unchanged. Current techniques typically rely on the feature description vector extracted by SIFT or similar descriptors. Many feature detectors, however, also estimate the orientation and scale of features, which are valuable guides to correspondence. This paper outlines a technique for exploiting relative orientation and scale to improve feature matching performance. It is shown that these cues can significantly improve matching performance, as measured by the percentage of inliers, across a range of different image transforms and object recognition rates.