B
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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Journal ArticleDOI
Learning in the dark: 3D integral imaging object recognition in very low illumination conditions using convolutional neural networks
Adam Markman,Bahram Javidi +1 more
TL;DR: To the best of the knowledge, this is the first report of utilizing 3D InIm and convolutional neural networks for 3D training and 3D object classification under very low illumination conditions.
Journal ArticleDOI
Super-resolved microsphere-assisted Mirau digital holography by oblique illumination
Vahid Abbasian,Yasaman Ganjkhani,Ehsan Ahadi Akhlaghi,Arun Anand,Bahram Javidi,Alireza Moradi +5 more
TL;DR: In this article, oblique illumination is used to improve the lateral resolution and edge sharpness in microsphere (MS)-assisted Mirau digital holographic microscopy (Mirau-DHM).
Journal ArticleDOI
Head Tracking Three-Dimensional Integral Imaging Display Using Smart Pseudoscopic-to-Orthoscopic Conversion
TL;DR: By performing the rotation transformation in the simulated display, viewing a 3D scene with head rotation can be realized for robust display and experimental results verify the feasibility of the proposed SPOC method.
Journal ArticleDOI
Optical 4D signal detection in turbid water by multi-dimensional integral imaging using spatially distributed and temporally encoded multiple light sources
TL;DR: The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods in signal detection and statistical analysis shows improved performance of the proposed multi-source integral imaging underwater system.
Proceedings ArticleDOI
Performance assessment of frequency plane filters applied to track association and sensor registration
TL;DR: In this article, the authors address an architecture that solves the track correlation problem using frequency plane pattern recognition techniques that also can provide CID capability and discuss statistical considerations and performance issues.