Extraction of Oblique Structures in Noisy Schlieren Sequences Using Computer Vision Techniques

As schlieren image data quantity has increased with faster frame rates, we are now faced with literally thousands of images to analyze. This presents an opportunity to study global flow structures over time that may not be evident from traditional surface measurements. Oblique structures, such as shock waves and contact surfaces, which give critical flowfield information, are common in many of these images. As data sets have become large, a degree of automation is desirable to extract these features to derive information on their behavior through the sequence. This paper employs a methodology based on computer vision techniques to provide an empirical estimate of oblique structure angles through an unsteady sequence. The methodology has been applied to a complex flowfield with multiple shock structures in a small region of interest (88 x 128 pixels). This study obtains a converged detection success rate of 94% and 97% for these structures and shows that computer vision techniques can be effective for the evaluation of optical data sets.