Efficient subpixel refinement with symbolic linear predictors

Vincent Lui, Jonathon Geeves, Winston Yii, Tom Drummond

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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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
EditorsDavid Forsyth, Ivan Laptev, Aude Oliva, Deva Ramanan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages8165-8173
Number of pages9
ISBN (Electronic)9781538664209
ISBN (Print)9781538664216
DOIs
Publication statusPublished - 2018
EventIEEE Conference on Computer Vision and Pattern Recognition 2018 - Salt Lake City, United States of America
Duration: 19 Jun 201821 Jun 2018
http://cvpr2018.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/8576498/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2018
Abbreviated titleCVPR 2018
CountryUnited States of America
CitySalt Lake City
Period19/06/1821/06/18
Internet address

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