Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar Imagery
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Title
Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar ImageryDate
2020-08-12Publisher
Institute of Electrical and Electronics EngineersISSN
2169-3536Bibliographic citation
TRAVER, V. Javier; PAREDES, Roberto. Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar Imagery. IEEE Access, 2020, vol. 8, p. 149122-149132.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/abstract/document/9165730Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
The problem of motion estimation from images has been widely studied in the past. Although
many mature solutions exist, there are still open issues and challenges to be addressed. For instance, in spite of
the ... [+]
The problem of motion estimation from images has been widely studied in the past. Although
many mature solutions exist, there are still open issues and challenges to be addressed. For instance, in spite of
the well-known performance of convolutional neural networks (CNNs) in many computer vision problems,
only very recent work has started to explore CNNs to learning to estimate motion, as an alternative to
manually-designed algorithms. These few initial efforts, however, have focused on conventional Cartesian
images, while other imaging models have not been studied. This work explores the yet unknown role of CNNs
in estimating global parametric motion in log-polar images. Despite its favourable properties, estimating
some motion components in this model has proven particularly challenging with past approaches. It is
therefore highly important to understand how CNNs behave when their input are log-polar images, since
they involve a complex mapping in the motion model, a polar image geometry, and space-variant resolution.
To this end, a CNN is considered in this work for regressing the motion parameters. Experiments on existing
image datasets using synthetic image deformations reveal that, interestingly, standard CNNs can successfully
learn to estimate global parametric motion on log-polar images with accuracies comparable to or better than
with Cartesian images. [-]
Investigation project
UJI-B2018-44 ; RED2018-102511-TRights
info:eu-repo/semantics/openAccess
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