Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
1-2013
Abstract
We propose a generic method for obtaining nonparametric image warps from noisy point correspondences. Our formulation integrates a huber function into a motion coherence framework. This makes our fitting function especially robust to piecewise correspondence noise (where an image section is consistently mismatched). By utilizing over parameterized curves, we can generate realistic nonparametric image warps from very noisy correspondence. We also demonstrate how our algorithm can be used to help stitch images taken from a panning camera by warping the images onto a virtual push-broom camera imaging plane.
Keywords
curve fitting, matching, non-parametric, spline; warping
Discipline
Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Proceedings of the 14th IEEE International Conference on Computer Vision, ICCV 2013, Sydney, December 1-8
First Page
2376
Last Page
2383
ISBN
9781479928392
Identifier
10.1109/ICCV.2013.295
Publisher
IEEE
City or Country
Sydney, Australia
Citation
LIN, Wen-yan; CHENG, Ming-Ming; ZHENG, Shuai; LU, Jiangbo; and CROOK, Nigel.
Robust non-parametric data fitting for correspondence modeling. (2013). Proceedings of the 14th IEEE International Conference on Computer Vision, ICCV 2013, Sydney, December 1-8. 2376-2383.
Available at: https://ink.library.smu.edu.sg/sis_research/4809
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICCV.2013.295