Automating shockwave segmentation in low-contrast coherent shadowgraphy

The paper presents a novel method for automatic segmentation of the low-contrast shadowgraphs that are acquired during the examination of the laser-induced shockwaves evolution. The method is based on two-stage, active-contour algorithms. First stage ensures global robustness, but it is locally inaccurate. It is implemented by traditional snake based on texture cues. The outcome serves as initialization to the second refining stage detection. In the second stage the detection is robust only locally and improves local accuracy. To do this, we introduce a greedy-snake algorithm. Local optimum is searched with respect to responses of steerable filtering and edge orientation similarity by exploiting the Bayesian formalism. The paper presents validation of the method on large data set of low-contrast shadowgraphs by comparison to the manual segmentation technique. The obtained results demonstrate overall good performance, robustness, high accuracy, and objectivity of the method.