Three-dimensional (3D) visualization and recognition using truncated photon counting model and integral imaging

摘要

In this paper, a statistical approach for three-dimensional (3D) visualization and recognition of photon-starved events based on a parametric estimator is overviewed. A truncated Poisson probability density function is considered for modeling the distribution of a few photons count observation. For 3D visualization and recognition of photon-starved events, an integral imaging, maximum likelihood estimator (MLE) and statistical inference algorithms are employed. It is shown in experiments that the parametric MLE using a truncated Poisson model for estimating the average number of photons for each voxel of a 3D object has a small estimation error compared with the MLE using a Poisson model and 3D recognition performance for photon-starved events can be enhanced by using the presented method.