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- W2799193406 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." @default.
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- W2799193406 date "2018-06-01" @default.
- W2799193406 modified "2023-09-23" @default.
- W2799193406 title "Efficient Subpixel Refinement with Symbolic Linear Predictors" @default.
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- W2799193406 doi "https://doi.org/10.1109/cvpr.2018.00852" @default.
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