Systems Neuroscience and Neurotechnology Unit, Neurocenter, Faculty of Medicine, Saarland University & School of Engineering, htw saar, Germany; Summer Program, Japan Society for the Promotion of Science (JSPS), Tokyo, Japan.
Systems Neuroscience and Neurotechnology Unit, Neurocenter, Faculty of Medicine, Saarland University & School of Engineering, htw saar, Germany; Department of Psychology, University of Hawai'i at Mānoa, United States.
J Neurosci Methods. 2021 Apr 1;353:109076. doi: 10.1016/j.jneumeth.2021.109076. Epub 2021 Jan 20.
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.
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.
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 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.
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.
在信号分析和模式匹配的背景下，一维信号的对齐对于信号形态的比较是一个重要问题。对于图像处理和计算机视觉，二维光流 (OF) 方法在运动分析和图像配准中得到了广泛的应用，并且变分 OF 方法在过去几十年中不断得到改进。
我们提出了一种用于一维信号对齐和位移估计的变分方法。我们将非平面位移的估计表示为一个具有相似性和平滑性项的优化问题，类似于变分 OF 估计。为此，我们可以利用有效的优化策略，允许在消费级硬件上进行实时应用。
我们将我们的方法应用于功能神经影像学的两个应用：双光子成像线扫描记录的对齐和单试次矩阵中诱发和事件相关电位的去噪。我们可以报告在对齐质量和计算速度方面的最先进的结果。
现有的一维对齐方法主要针对恒定位移，不允许本地亚采样精度或对正则化进行精确控制，或者比所提出的方法慢。
我们的方法作为一个 MATLAB 工具箱实现，并在线可用。它适用于需要高精度和高速的一维对齐问题，并且会出现非恒定位移。
