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.
Original language | English |
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Title of host publication | Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Editors | David Forsyth, Ivan Laptev, Aude Oliva, Deva Ramanan |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 8165-8173 |
Number of pages | 9 |
ISBN (Electronic) | 9781538664209 |
ISBN (Print) | 9781538664216 |
DOIs | |
Publication status | Published - 2018 |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2018 - Salt Lake City, United States of America Duration: 19 Jun 2018 → 21 Jun 2018 http://cvpr2018.thecvf.com/ https://ieeexplore.ieee.org/xpl/conhome/8576498/proceeding (Proceedings) |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2018 |
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Abbreviated title | CVPR 2018 |
Country | United States of America |
City | Salt Lake City |
Period | 19/06/18 → 21/06/18 |
Internet address |