@InProceedings{7362457,
  author = {R. {Ben Salah} and O. Alata and B. Tremblais and L. Thomas and L. David},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Particle volume reconstruction based on a marked point process and application to TOMO-PIV},
  year = {2015},
  pages = {619-623},
  abstract = {In this paper, we propose a new tomographic reconstruction method, called IOD-PVRMPP, to reconstruct 3D particle volumes from 2D particle images provided by the Tomographic Particle Image Ve-locimetry (Tomo-PIV) technique. Our method, based on marked point processes (or object processes), allows to solve the problem in a parsimonious way. It facilitates the introduction of prior knowledge and solves memory problem which is inherent to voxel based approaches used by classical tomographic reconstruction methods. The reconstruction of a 3D particle set is obtained by minimizing an energy function which defines the marked point process. To this aim, we use a simulated annealing algorithm based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. To speed up the convergence of the simulated annealing, we develop an initialization method which provides the initial distribution of 3D particles. To do that, we proceed by detecting 2D particles located in projection images. Using synthetic data, we show that IOD-PVRMPP method gives better results than MinLOS-MART method for different seeding densities.},
  keywords = {image reconstruction;Markov processes;Monte Carlo methods;simulated annealing;tomography;initialization methodparticle volume reconstruction;reversible jump Markov chain Monte Carlo method;simulated annealing algorithm;marked point processes;tomographic particle image velocimetry technique;2D particle images;3D particle volumes;tomographic reconstruction method;TOMO-PIV;Image reconstruction;Three-dimensional displays;Reconstruction algorithms;Simulated annealing;Europe;Signal processing;Marked Point Processes or Object Processes;Tomography Reconstruction;Simulated Annealing;RJMCMC;Tomo-PIV},
  doi = {10.1109/EUSIPCO.2015.7362457},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104905.pdf},
}

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Our method, based on marked point processes (or object processes), allows to solve the problem in a parsimonious way. It facilitates the introduction of prior knowledge and solves memory problem which is inherent to voxel based approaches used by classical tomographic reconstruction methods. The reconstruction of a 3D particle set is obtained by minimizing an energy function which defines the marked point process. To this aim, we use a simulated annealing algorithm based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. To speed up the convergence of the simulated annealing, we develop an initialization method which provides the initial distribution of 3D particles. To do that, we proceed by detecting 2D particles located in projection images. 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Thomas and L. David},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Particle volume reconstruction based on a marked point process and application to TOMO-PIV},\n year = {2015},\n pages = {619-623},\n abstract = {In this paper, we propose a new tomographic reconstruction method, called IOD-PVRMPP, to reconstruct 3D particle volumes from 2D particle images provided by the Tomographic Particle Image Ve-locimetry (Tomo-PIV) technique. Our method, based on marked point processes (or object processes), allows to solve the problem in a parsimonious way. It facilitates the introduction of prior knowledge and solves memory problem which is inherent to voxel based approaches used by classical tomographic reconstruction methods. The reconstruction of a 3D particle set is obtained by minimizing an energy function which defines the marked point process. To this aim, we use a simulated annealing algorithm based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. To speed up the convergence of the simulated annealing, we develop an initialization method which provides the initial distribution of 3D particles. To do that, we proceed by detecting 2D particles located in projection images. 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