His main research concerns Artificial intelligence, Computer vision, Pixel, Retina and Neuromorphic engineering. 3D single-object recognition, Cognitive neuroscience of visual object recognition, Feature, Teleoperation and Haptic technology are the subjects of his Artificial intelligence studies. Ryad Benosman combines topics linked to Temporal resolution with his work on Computer vision.
Ryad Benosman combines subjects such as Monocular, Robotics, High dynamic range, Probabilistic logic and Smart camera with his study of Pixel. The Retina study combines topics in areas such as Endocrinology and Vein occlusion, Retinal. His Neuromorphic engineering study incorporates themes from Algorithm, Eye tracking and Robustness.
His primary areas of study are Artificial intelligence, Computer vision, Neuromorphic engineering, Pixel and Retina. His biological study spans a wide range of topics, including Computation and Pattern recognition. His Computer vision study frequently draws parallels with other fields, such as Computer graphics.
His studies deal with areas such as Algorithm, Neural coding and Spiking neural network as well as Neuromorphic engineering. His Pixel research incorporates themes from Luminance and Temporal resolution. His work deals with themes such as Retinal and Optogenetics, which intersect with Retina.
His main research concerns Artificial intelligence, Neuromorphic engineering, Computer vision, Spiking neural network and Optogenetics. His study brings together the fields of Pattern recognition and Artificial intelligence. His Pattern recognition research incorporates elements of Spike sorting, Computation and Computer simulation.
In the field of Computer vision, his study on Pixel, Optical flow and Image sensor overlaps with subjects such as Population. He interconnects Distributed computing and Sensory system in the investigation of issues within Spiking neural network. His work in Optogenetics tackles topics such as Stimulus which are related to areas like Visual acuity, Retinal ganglion, Retinitis pigmentosa and Visual perception.
His primary areas of investigation include Computer vision, Artificial intelligence, Retina, Neuromorphic engineering and Macular degeneration. Ryad Benosman studies Gesture recognition which is a part of Computer vision. He is studying Gesture, which is a component of Artificial intelligence.
His Retina research includes elements of Computational neuroscience, Pixel, Machine vision and Spiking neural network. His Neuromorphic engineering research is multidisciplinary, relying on both Luminance, Orientation, Line, Optical flow and Line fitting. The concepts of his Macular degeneration study are interwoven with issues in Retinal, Retinal Dystrophies, Prosthesis, Photic Stimulation and Eye Movement Measurements.
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Event-Based Visual Flow
Ryad Benosman;Charles Clercq;Xavier Lagorce;Sio-Hoi Ieng.
IEEE Transactions on Neural Networks (2014)
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition
Xavier Lagorce;Garrick Orchard;Francesco Galluppi;Bertram E. Shi.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Panoramic vision : sensors, theory, and applications
Ryad Benosman;Sing Bing Kang.
HFirst: A Temporal Approach to Object Recognition
Garrick Orchard;Cedric Meyer;Ralph Etienne-Cummings;Christoph Posch.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Amos Sironi;Manuele Brambilla;Nicolas Bourdis;Xavier Lagorce.
computer vision and pattern recognition (2018)
Asynchronous frameless event-based optical flow
Ryad Benosman;Sio-Hoi Ieng;Charles Clercq;Chiara Bartolozzi.
Neural Networks (2012)
Simultaneous mosaicing and tracking with an event camera
Hanme Kim;Ankur Handa;Ryad Benosman;Sio Hoi Ieng.
british machine vision conference (2014)
Asynchronous Event-Based Binocular Stereo Matching
P. Rogister;R. Benosman;Sio-Hoi Ieng;Patrick Lichtsteiner.
IEEE Transactions on Neural Networks (2012)
Multiplex Cell and Lineage Tracking with Combinatorial Labels
Karine Loulier;Karine Loulier;Karine Loulier;Raphaëlle Barry;Raphaëlle Barry;Raphaëlle Barry;Pierre Mahou;Pierre Mahou;Pierre Mahou;Yann Le Franc;Yann Le Franc;Yann Le Franc.
Asynchronous Event-Based Visual Shape Tracking for Stable Haptic Feedback in Microrobotics
Zhenjiang Ni;A. Bolopion;J. Agnus;R. Benosman.
IEEE Transactions on Robotics (2012)
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