Tomographic Reconstruction of 3D Objects Using Marked Point Process Framework

作者：Ben Salah Riadh; Alata Olivier^*; Tremblais Benoit; Thomas Lionel; David Laurent

来源：Journal of Mathematical Imaging and Vision, 2018, 60(7): 1132-1149.

DOI：10.1007/s10851-018-0800-6

• 3D object reconstruction
• Stochastic models
• Marked point processes
• Metropolis-Hastings-Green (MHG) algorithm
• Simulated annealing
• Tomographic PIV

For reconstructing sparse volumes of 3D objects from projection images taken from different viewing directions, several volumetric reconstruction techniques are available. Most popular volume reconstruction methods are algebraic algorithms (e.g. the multiplicative algebraic reconstruction technique, MART). These methods which belong to voxel-oriented class allow volume to be reconstructed by computing each voxel intensity. A new class of tomographic reconstruction methods, called "object-oriented" approach, has recently emerged and was used in the Tomographic Particle Image Velocimetry technique (Tomo-PIV). In this paper, we propose an object-oriented approach, called Iterative Object Detection-Object Volume Reconstruction based on Marked Point Process (IOD-OVRMPP), to reconstruct the volume of 3D objects from projection images of 2D objects. Our approach allows the problem to be solved in a parsimonious way by minimizing an energy function based on a least squares criterion. Each object belonging to 2D or 3D space is identified by its continuous position and a set of features (marks). In order to optimize the population of objects, we use a simulated annealing algorithm which provides a "Maximum A Posteriori" estimation. To test our approach, we apply it to the field of Tomo-PIV where the volume reconstruction process is one of the most important steps in the analysis of volumetric flow. Finally, using synthetic data, we show that the proposed approach is able to reconstruct densely seeded flows.

• 出版日期2018-9