What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Optics
- Computer vision
Her main research concerns Artificial intelligence, Computer vision, Optics, Image processing and Spatial frequency.
The various areas that Eli Peli examines in her Artificial intelligence study include Low vision, Sensitivity and Pattern recognition.
Her Computer vision research includes themes of Visual field and Computer graphics.
Her Optics research includes elements of Image quality, Psychophysics and Flicker.
Eli Peli has researched Image processing in several fields, including Age-related maculopathy, Maculopathy, Drusen, Fundus photography and Reproducibility.
Her Spatial frequency study integrates concerns from other disciplines, such as Optical transfer function, Nuclear magnetic resonance and Scale invariance.
Her most cited work include:
- Contrast in complex images. (1069 citations)
- Image enhancement using a contrast measure in the compressed domain (257 citations)
- Reading with a macular scotoma. I. Retinal location of scotoma and fixation area. (247 citations)
What are the main themes of her work throughout her whole career to date?
Eli Peli focuses on Artificial intelligence, Computer vision, Optics, Optometry and Visual field.
Her Artificial intelligence research is multidisciplinary, incorporating elements of Low vision and Pattern recognition.
Her work carried out in the field of Computer vision brings together such families of science as Visual perception, Tunnel vision, Computer graphics and Eye movement.
Her work on Spatial frequency and Prism as part of general Optics study is frequently linked to Field, bridging the gap between disciplines.
Her research integrates issues of Ophthalmology, Visual acuity, Fundus and Binocular vision in her study of Optometry.
Her research in Visual field intersects with topics in Simulation and Blind spot.
She most often published in these fields:
- Artificial intelligence (42.46%)
- Computer vision (38.98%)
- Optics (18.10%)
What were the highlights of her more recent work (between 2014-2021)?
- Artificial intelligence (42.46%)
- Computer vision (38.98%)
- Optics (18.10%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Artificial intelligence, Computer vision, Optics, Visual field and Driving simulator.
Eli Peli combines subjects such as Pedestrian and Pattern recognition with her study of Artificial intelligence.
Her work on Gaze, Parallax and Stereoscopy as part of her general Computer vision study is frequently connected to Clutter and Cluttering, thereby bridging the divide between different branches of science.
The Prism, Visual field loss and Chromatic aberration research Eli Peli does as part of her general Optics study is frequently linked to other disciplines of science, such as Field, therefore creating a link between diverse domains of science.
Her work investigates the relationship between Visual field and topics such as Physical medicine and rehabilitation that intersect with problems in Perceptual motor and Hemianopsia.
Her studies deal with areas such as Pedestrian detection, Optometry, Visual acuity and Visual perception as well as Driving simulator.
Between 2014 and 2021, her most popular works were:
- Active confocal imaging for visual prostheses. (29 citations)
- The risk of pedestrian collisions with peripheral visual field loss. (22 citations)
- Tunnel Vision Prismatic Field Expansion: Challenges and Requirements. (17 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Optics
- Computer vision
Her primary scientific interests are in Artificial intelligence, Visual field, Driving simulator, Computer vision and Blind spot.
Her Artificial intelligence study frequently draws connections to other fields, such as Pattern recognition.
The study incorporates disciplines such as Motion perception, Hemispatial neglect, Confocal and Image processing in addition to Visual field.
Her study in Driving simulator is interdisciplinary in nature, drawing from both Pedestrian detection, Visual acuity and Glare.
She has included themes like Geometric modeling and Sensory substitution in her Computer vision study.
Her Blind spot research is multidisciplinary, relying on both Prism, Visual perception and Percept.
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