What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
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 most cited work include:
- Event-Based Visual Flow (169 citations)
- HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition (168 citations)
- Panoramic vision : sensors, theory, and applications (155 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (88.89%)
- Computer vision (60.44%)
- Neuromorphic engineering (39.11%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (88.89%)
- Neuromorphic engineering (39.11%)
- Computer vision (60.44%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Behavioural responses to a photovoltaic subretinal prosthesis implanted in non-human primates. (20 citations)
- Behavioural responses to a photovoltaic subretinal prosthesis implanted in non-human primates. (20 citations)
- Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities. (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
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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