Parametric Object Motion from Blur

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 c...

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Bibliographic Details
Published in:2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1846 - 1854
Main Authors: Gast, Jochen, Sellent, Anita, Roth, Stefan
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2016
Subjects:
Cameras
Computer vision
Estimation
Image segmentation
Kernel
Motion segmentation
Optical imaging
ISSN:1063-6919
Online Access:Get full text
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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.204