Deep learning technique, has made tremendous progress in fluid mechanics in recent
years, because of its mighty feature extraction capacity from complicated and massive fluid …

Variational approaches to image motion segmentation has been an active field of study in
image processing and computer vision for two decades. We present a short overview over …

In this paper, we propose a supervised learning strategy for the fluid motion estimation
problem (ie, extracting the velocity fields from particle images). The purpose of this work is to …

Understanding the mechanics of blood flow is necessary for develo** insights into
mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of …

Particle image velocimetry (PIV), as a common technology for analyzing the global flow
motion from images, plays a significant role in experimental fluid mechanics. In this article …

We report a new workflow for background-oriented schlieren (BOS), termed “physics-
informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and …

The connection between fluid flow and optical flow is explored in typical flow visualizations
to provide a rational foundation for application of the optical flow method to image-based …

Our contribution deals with fast computation of dense two-component (2C) PIV vector fields
using Graphics Processing Units (GPUs). We show that iterative gradient-based cross …

Velocity estimation (extracting the displacement vector information) from the particle image
pairs is of critical importance for particle image velocimetry. This problem is mostly …

This paper presents direct comparisons between the physics-based optical flow and well-
established cross-correlation methods for extraction of velocity fields from particle images …