1 research outputs found
Efficient Subpixel Refinement with Symbolic Linear Predictors
We present an efficient subpixel refinement method usinga learning-based
approach called Linear Predictors. Two key ideas are shown in this paper.
Firstly, we present a novel technique, called Symbolic Linear Predictors, which
makes the learning step efficient for subpixel refinement. This makes our
approach feasible for online applications without compromising accuracy, while
taking advantage of the run-time efficiency of learning based approaches.
Secondly, we show how Linear Predictors can be used to predict the expected
alignment error, allowing us to use only the best keypoints in resource
constrained applications. We show the efficiency and accuracy of our method
through extensive experiments.Comment: IEEE/CVF International Conference on Computer Vision and Pattern
Recognition 2018 (CVPR