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Bahram Javidi
Researcher at University of Connecticut
Publications - 953
Citations - 32166
Bahram Javidi is an academic researcher from University of Connecticut. The author has contributed to research in topics: Integral imaging & Digital holography. The author has an hindex of 84, co-authored 937 publications receiving 29681 citations. Previous affiliations of Bahram Javidi include National University of Ireland & Hanscom Air Force Base.
Papers
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Proceedings ArticleDOI
Is it worth using an array of cameras to capture the spatio-angular information of a 3D scene or is it enough with just two?
Hector Navarro,Adrian Dorado,Genaro Saavedra,Anabel Llavador,Manuel Martínez-Corral,Bahram Javidi +5 more
TL;DR: It is demonstrated that InI is the optimum system to sampling the spatio-angular information contained in a 3D scene.
Journal ArticleDOI
Analysis of image detection based on fourier plane nonlinear filtering in a joint transform correlator.
TL;DR: This study analyzes the performance of the nonlinear joint transform correlator in terms of the output signal-to-noise ratio, and finds that the smooth square-root Fourier plane nonlinearity offers extremely robust performance with respect to relative noise bandwidth.
Book ChapterDOI
Design of Distortion-Invariant Optical ID Tags for Remote Identification and Verification of Objects
Journal ArticleDOI
Three-Dimensional Holographic Display Using Dense Ray Sampling and Integral Imaging Capture
TL;DR: In this article, a high-resolution three-dimensional (3D) holographic display system based on dense ray sampling and integral imaging capture is presented. And the authors demonstrate a depth of field extension technique during the integral image capture by combining an amplitude modulated lens and a blind convolution approach.
Journal ArticleDOI
Minimum-error-probability receiver for detecting a noisy target in colored background noise
TL;DR: The design of an optimum receiver to detect a noisy target with unknown illumination in nonoverlapping colored background noise is described, designed on the basis of binary Bayesian hypothesis testing with unknown parameters.