Efficient Subpixel Refinement with Symbolic Linear Predictors
This work addresses efficiency challenges in subpixel refinement for resource-constrained applications, though it appears incremental as it builds on existing learning-based approaches.
The paper tackles the problem of subpixel refinement in computer vision by introducing Symbolic Linear Predictors, which improve efficiency for online applications while maintaining accuracy, as demonstrated through extensive experiments.
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.