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Yann Frauel
Researcher at National Autonomous University of Mexico
Publications - 41
Citations - 2119
Yann Frauel is an academic researcher from National Autonomous University of Mexico. The author has contributed to research in topics: Digital holography & Holography. The author has an hindex of 15, co-authored 41 publications receiving 2036 citations. Previous affiliations of Yann Frauel include National University of Ireland & Maynooth University.
Papers
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Resistance of the double random phase encryption against various attacks
TL;DR: A technique to recover the exact keys with only two known plain images is described, and this technique is compared to other attacks proposed in the literature.
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Compression of digital holograms for three-dimensional object reconstruction and recognition
TL;DR: This work quantifies the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.
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Real-time three-dimensional object reconstruction by use of a phase-encoded digital hologram.
TL;DR: It is demonstrated that it is possible to reconstruct optically 3D objects using only phase information of the optical field calculated from phase-shifting digital holograms.
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Three-Dimensional Imaging and Processing Using Computational Holographic Imaging
TL;DR: Applications of digital holography cover three-dimensional (3-D) imaging as well as several associated problems and optical and digital methods to reconstruct and visualize the recorded objects are described.
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A robust Graph Transformation Matching for non-rigid registration
Wendy Aguilar,Yann Frauel,Francisco Escolano,M. Elena Martinez-Perez,Arturo Espinosa-Romero,Miguel Angel Lozano +5 more
TL;DR: The proposed Graph Transformation Matching method, relying on finding a consensus nearest-neighbour graph emerging from candidate matches, is successfully applied in the context of constructing mosaics of retinal images, where feature points are extracted from properly segmented binary images.