Parametric Object Motion From Blur

Jochen Gast, Anita Sellent, Stefan Roth; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1846-1854

Abstract


Motion blur can adversely affect a number of vision tasks, hence it is generally considered a nuisance. We instead treat motion blur as a useful signal that allows to compute the motion of objects from a single image. Drawing on the success of joint segmentation and parametric motion models in the context of optical flow estimation, we propose a parametric object motion model combined with a segmentation mask to exploit localized, non-uniform motion blur. Our parametric image formation model is differentiable w.r.t. the motion parameters, which enables us to generalize marginal-likelihood techniques from uniform blind deblurring to localized, non-uniform blur. A two-stage pipeline, first in derivative space and then in image space, allows to estimate both parametric object motion as well as a motion segmentation from a single image alone. Our experiments demonstrate its ability to cope with very challenging cases of object motion blur.

Related Material


[pdf]
[bibtex]
@InProceedings{Gast_2016_CVPR,
author = {Gast, Jochen and Sellent, Anita and Roth, Stefan},
title = {Parametric Object Motion From Blur},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}