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Proceedings Paper

Bayesian estimation of depth information in three-dimensional integral imaging
Author(s): Xiao Xiao; Bahram Javidi; Dipak K. Dey
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Paper Abstract

In this paper, we propose a Bayesian framework to infer depths of object surfaces in a 3D integral imaging system. In a 3D integral imaging system, the depth of Lambertian surfaces can be estimated from the statistics of the spectral radiation pattern. However, the estimated depth may contain errors due to system uncertainties. To better infer the depth information, we utilize a Bayesian framework and a Markov Random Field (MRF) model with the knowledge of the statistical information of object intensities and the assumption that object surfaces are smooth. In the proposed method, we combine a Bayesian framework and the characteristics of 3D integral imaging systems to infer the depths. Simulated and experimental results illustrate the performance of the proposed method.

Paper Details

Date Published: 5 June 2014
PDF: 8 pages
Proc. SPIE 9117, Three-Dimensional Imaging, Visualization, and Display 2014, 911714 (5 June 2014); doi: 10.1117/12.2050544
Show Author Affiliations
Xiao Xiao, Univ. of Connecticut (United States)
Bahram Javidi, Univ. of Connecticut (United States)
Dipak K. Dey, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 9117:
Three-Dimensional Imaging, Visualization, and Display 2014
Bahram Javidi; Jung-Young Son; Osamu Matoba; Manuel Martínez-Corral; Adrian Stern, Editor(s)

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