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• W3157758045 abstract "Abstract Particle image velocimetry (PIV) is a powerful image correlation method for measuring bulk velocity fields of flowing media. It typically uses optical images, representing quasi-two-dimensional experimental slices, to measure a single velocity value at each in-plane position. However, projection-based imaging methods, such as x-ray radiography or shadowgraph imaging, encode additional out-of-plane information that regular PIV is unable to capture. Here, we introduce a new image analysis method, named deep velocimetry, that goes beyond established PIV methods and is capable of extracting full velocity distributions from projected images. The method involves solving a deconvolution inverse problem to recover the distribution at each in-plane position, and is validated using artificial data as well as controlled laboratory x-ray experiments. The additional velocity information delivered by deep velocimetry could provide new insight into a range of fluid and granular flows where out-of-plane variation is significant. Graphic abstract" @default.
• W3157758045 created "2021-05-10" @default.
• W3157758045 creator A5009518481 @default.
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• W3157758045 date "2021-04-22" @default.
• W3157758045 modified "2023-09-27" @default.
• W3157758045 title "Deep velocimetry: Extracting full velocity distributions from projected images of flowing media" @default.
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• W3157758045 doi "https://doi.org/10.1007/s00348-021-03203-w" @default.
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