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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 17, Iss. 18 — Aug. 31, 2009
  • pp: 15709–15715

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

Inkyu Moon and Bahram Javidi  »View Author Affiliations

Optics Express, Vol. 17, Issue 18, pp. 15709-15715 (2009)

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In this paper, a statistical approach is presented for three-dimensional (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.

© 2009 OSA

OCIS Codes
(030.5260) Coherence and statistical optics : Photon counting
(100.6890) Image processing : Three-dimensional image processing

ToC Category:
Image Processing

Original Manuscript: May 19, 2009
Revised Manuscript: July 8, 2009
Manuscript Accepted: August 7, 2009
Published: August 20, 2009

Inkyu Moon and Bahram Javidi, "Three dimensional imaging and recognition using truncated photon counting model and parametric maximum likelihood estimator," Opt. Express 17, 15709-15715 (2009)

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