Efficient subpixel refinement with symbolic linear predictors

Vincent Lui; Jonathon Geeves; Winston Yii; Tom Drummond

doi:10.1109/CVPR.2018.00852

Efficient subpixel refinement with symbolic linear predictors

Vincent Lui, Jonathon Geeves, Winston Yii, Tom Drummond

Electrical and Computer Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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	https://doi.org/10.1109/CVPR.2018.00852
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
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	http://cvpr2018.thecvf.com/ https://ieeexplore.ieee.org/xpl/conhome/8576498/proceeding (Proceedings)

Access to Document

10.1109/CVPR.2018.00852

Link to publication in Scopus

Cite this

Lui, V., Geeves, J., Yii, W., & Drummond, T. (2018). Efficient subpixel refinement with symbolic linear predictors. In D. Forsyth, I. Laptev, A. Oliva, & D. Ramanan (Eds.), Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8165-8173). (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPR.2018.00852

Lui, Vincent ; Geeves, Jonathon ; Yii, Winston ; Drummond, Tom. / Efficient subpixel refinement with symbolic linear predictors. Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. editor / David Forsyth ; Ivan Laptev ; Aude Oliva ; Deva Ramanan. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 8165-8173 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

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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.",

author = "Vincent Lui and Jonathon Geeves and Winston Yii and Tom Drummond",

year = "2018",

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booktitle = "Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition",

address = "United States of America",

note = "IEEE Conference on Computer Vision and Pattern Recognition 2018, CVPR 2018 ; Conference date: 19-06-2018 Through 21-06-2018",

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Lui, V, Geeves, J, Yii, W & Drummond, T 2018, Efficient subpixel refinement with symbolic linear predictors. in D Forsyth, I Laptev, A Oliva & D Ramanan (eds), Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 8165-8173, IEEE Conference on Computer Vision and Pattern Recognition 2018, Salt Lake City, United States of America, 19/06/18. https://doi.org/10.1109/CVPR.2018.00852

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

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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.

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Lui V, Geeves J, Yii W, Drummond T. Efficient subpixel refinement with symbolic linear predictors. In Forsyth D, Laptev I, Oliva A, Ramanan D, editors, Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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). https://doi.org/10.1109/CVPR.2018.00852