RT Journal Article
SR Electronic
T1 Fast Variational Alignment of non-flat 1D Displacements for Applications in Neuroimaging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.27.151522
DO 10.1101/2020.06.27.151522
A1 Philipp Flotho
A1 David Thinnes
A1 Bernd Kuhn
A1 Christopher J. Roome
A1 Jonas F. Vibell
A1 Daniel J. Strauss
YR 2020
UL http://biorxiv.org/content/early/2020/06/29/2020.06.27.151522.abstract
AB Background In the context of signal analysis and pattern matching, alignment of 1D signals for the comparison of signal morphologies is an important problem. For image processing and computer vision, 2D optical flow (OF) methods find wide application for motion analysis and image registration and variational OF methods have been continuously improved over the past decades.New Method We propose a variational method for the alignment and displacement estimation of 1D signals. We pose the estimation of non-flat displacements as an optimization problem with a similarity and smoothness term similar to variational OF estimation. To this end, we can make use of efficient optimization strategies that allow real-time applications on consumer grade hardware.Results We apply our method to two applications from functional neuroimaging: The alignment of 2-photon imaging line scan recordings and the denoising of evoked and event-related potentials in single trial matrices. We can report state of the art results in terms of alignment quality and computing speeds.Existing Methods Existing methods for 1D alignment target mostly constant displacements, do not allow native subsample precision or precise control over regularization or are slower than the proposed method.Conclusions Our method is implemented as a MATLAB toolbox and is online available. It is suitable for 1D alignment problems, where high accuracy and high speed is needed and non-constant displacements occur.Competing Interest StatementThe authors have declared no competing interest.