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• W2023495251 abstract "In recent years, marked point processes have received a great deal of attention. They were applied with success to extract objects in large data sets as those obtained in remote sensing frameworks or biological studies. We propose in this paper a method based on marked point processes to reconstruct volumes of 3D particles from images of 2D particles provided by the Tomographic Particle Image Velocimetry (Tomo-PIV) technique. Unlike other reconstruction methods, our approach allows us to solve the problem in a parsimonious way. It facilitates the introduction of prior knowledge and naturally solves the memory problem which is inherent to pixel based approach used by classical tomographic reconstruction methods. The best reconstruction is found by minimizing an energy function which defines the marked point process. In order to avoid local minima, we use a simulated annealing algorithm. Results are presented on simulated data." @default.
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• W2023495251 date "2014-05-01" @default.
• W2023495251 modified "2023-09-24" @default.
• W2023495251 title "3D particle volume tomographic reconstruction based on marked point process: Application to Tomo-PIV in fluid mechanics" @default.
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• W2023495251 doi "https://doi.org/10.1109/icassp.2014.6855190" @default.
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