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An improved image processing method for particle characterization by shadowgraphy

Abstract : Shadowgraphy is one of the most popular imaging techniques to characterize moving particles by their size, geometry as well as velocity, due to its simplicity. However, it requires advanced image processing to handle various image defects such as non-uniform illumination, overlapped particles, etc., which are normally only solved for individual applications. This study proposes a robust image processing method for particle shadowgraphy, aiming to process imperfect particle shadow images. The proposed method first detects qualified particles from particle shadow images, and then processes detected particles individually. Therefore different defects from different particles can be handled separately and locally. An overlapped particles detection and separation algorithm is also implemented to improve the accuracy of size and geometry characterization.The proposed method is first proved by synthetic generated particle shadow images, followed by a proof test with shadow images from a transparent dot pattern target. Finally this method is successfully applied to a shadowimage acquired from a water spray and proved to be able to handle various issues of shadowgraphy.
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Submitted on : Saturday, May 16, 2020 - 3:40:19 PM
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H. Wang, F. Felis, S. Tomas, Fabien Anselmet, M. Amielh. An improved image processing method for particle characterization by shadowgraphy. ILASS-Europe 2017 28th Conference on Liquid Atomization and Spray Systems, Sep 2017, Valencia, Spain. pp.6, ⟨10.4995/ILASS2017.2017.4614⟩. ⟨hal-02608221⟩



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