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Automating shockwave segmentation in low-contrast coherent shadowgraphy

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Abstract

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.

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Acknowledgments

We would like to thank Dr.Matej Kristan from Visual Cognitive Systems Laboratory, University of Ljubljana, for fruitful discussions and help.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Jaka Pribošek, Peter Gregorčič & Janez Diaci

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  1. Jaka Pribošek
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  2. Peter Gregorčič
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  3. Janez Diaci
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Correspondence to Jaka Pribošek.

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Pribošek, J., Gregorčič, P. & Diaci, J. Automating shockwave segmentation in low-contrast coherent shadowgraphy. Machine Vision and Applications 26, 485–494 (2015). https://doi.org/10.1007/s00138-015-0683-0

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  • DOI: https://doi.org/10.1007/s00138-015-0683-0

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