Three dimensional imaging and recognition using truncated photon counting model and parametric maximum likelihood estimator

In this paper, a statistical approach is presented for threedimensional (3D) visualization and recognition of objects having very small number of photons based on a parametric estimator. A truncated Poisson probability density function is assumed for modeling the distribution of small number of photons count observation. For 3D visualization and recognition of photon-limited objects, an integral imaging system is employed. We utilize virtual geometrical ray propagation for 3D reconstruction of objects. A maximum likelihood estimator (MLE) and statistical inference algorithms are applied to small number of photons counted elemental images captured with integral imaging. We have demonstrated that the MLE using a truncated Poisson model for estimating the average number of photon for each voxel of a photon starved 3D object has a small estimation error compared with the MLE using a Poisson model. Also, we present experiments to investigate the effect of 3D sensing parallax on the recognition performance under a fixed mean number of photons.