Abstract
We present an efficient subpixel refinement method using
a 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