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 |
|---|---|
| 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/ |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| ISSN (Print) | 1063-6919 |
Conference
| Conference | IEEE Conference on Computer Vision and Pattern Recognition 2018 |
|---|---|
| Abbreviated title | CVPR 2018 |
| Country | United States of America |
| City | Salt Lake City |
| Period | 19/06/18 → 21/06/18 |
| Internet address |
Cite this
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Efficient subpixel refinement with symbolic linear predictors. / Lui, Vincent; Geeves, Jonathon; Yii, Winston; Drummond, Tom.
Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. ed. / David Forsyth; Ivan Laptev; Aude Oliva; Deva Ramanan. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 8165-8173 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Efficient subpixel refinement with symbolic linear predictors
AU - Lui, Vincent
AU - Geeves, Jonathon
AU - Yii, Winston
AU - Drummond, Tom
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062854478&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2018.00852
DO - 10.1109/CVPR.2018.00852
M3 - Conference Paper
AN - SCOPUS:85062854478
SN - 9781538664216
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 8165
EP - 8173
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
A2 - Forsyth, David
A2 - Laptev, Ivan
A2 - Oliva, Aude
A2 - Ramanan, Deva
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -