Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics

arXiv:2404.15497v1 Announce Type: cross
Abstract: Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motions of objects at the pixel level. In this article, we evaluate the ability of optical flow to quantify the spontaneous flows of MT-based active nematics under different labeling conditions. We compare DLOF against the commonly used technique, particle imaging velocimetry (PIV). We obtain flow velocity ground truths either by performing …

abstract article arxiv cond-mat.soft convolutional neural networks cs.lg deep learning features fields flow networks neural networks objects optical optical flow pixel type video