Density reconstruction from schlieren images through Bayesian nonparametric models
Fluid Dynamics
2022-08-23 v3 Computer Vision and Pattern Recognition
Abstract
This study proposes a radically alternate approach for extracting quantitative information from schlieren images. The method uses a scaled, derivative enhanced Gaussian process model to obtain true density estimates from two corresponding schlieren images with the knife-edge at horizontal and vertical orientations. We illustrate our approach on schlieren images taken from a wind tunnel sting model, a supersonic aircraft in flight, and a high-order numerical shock tube simulation.
Cite
@article{arxiv.2201.05233,
title = {Density reconstruction from schlieren images through Bayesian nonparametric models},
author = {Bryn Noel Ubald and Pranay Seshadri and Andrew Duncan},
journal= {arXiv preprint arXiv:2201.05233},
year = {2022}
}