Improving Keypoint Orientation Assignment
Steffen Gauglitz, Matthew Turk, Tobias Höllerer · 2011
Detection and description of local image features has proven to be a powerful paradigm for a variety of applications in computer vision. Often, this process includes an orientation assignment step to render the overall process invariant to in-plane rotation. In this paper, we review several different existing algorithms and propose two novel, efficient methods for orientation assignment. The first method exhibits a very good speedperformance trade-off; the second is capable of multiple orientations and performs comparable to SIFT’s orientation assignment while being significantly cheaper. Additionally, we improve one of the existing orientation assignment methods by generalizing it. All algorithms are evaluated empirically under a variety of conditions and in combination with six keypoint detectors.