Shearlet-Based Detection of Flame Fronts
Abstract
Identifying and characterizing flame fronts is the most common task in the computer-assisted analysis of data obtained from imaging techniques such as planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or particle imaging velocimetry (PIV). We present a novel edge and ridge (line) detection algorithm based on complex-valued wavelet-like analyzing functions -- so-called complex shearlets -- displaying several traits useful for the extraction of flame fronts. In addition to providing a unified approach to the detection of edges and ridges, our method inherently yields estimates of local tangent orientations and local curvatures. To examine the applicability for high-frequency recordings of combustion processes, the algorithm is applied to mock images distorted with varying degrees of noise and real-world PLIF images of both OH and CH radicals. Furthermore, we compare the performance of the newly proposed complex shearlet-based measure to well-established edge and ridge detection techniques such as the Canny edge detector, another shearlet-based edge detector, and the phase congruency measure.
Cite
@article{arxiv.1511.03753,
title = {Shearlet-Based Detection of Flame Fronts},
author = {Rafael Reisenhofer and Johannes Kiefer and Emily J. King},
journal= {arXiv preprint arXiv:1511.03753},
year = {2016}
}