What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
Shree K. Nayar spends much of his time researching Artificial intelligence, Computer vision, Computer graphics, Image processing and Image resolution.
Artificial intelligence and Radiance are commonly linked in his work.
His Computer vision study combines topics from a wide range of disciplines, such as Lens and Brightness.
His Computer graphics research integrates issues from Camera auto-calibration, Flash, Depth map and Light field.
His study focuses on the intersection of Image processing and fields such as Pattern recognition with connections in the field of Learning object.
Shree K. Nayar combines subjects such as Catadioptric sensor, Catadioptric system, Temporal resolution and Iterative reconstruction with his study of Image resolution.
His most cited work include:
- Visual learning and recognition of 3-D objects from appearance (1833 citations)
- Attribute and simile classifiers for face verification (1125 citations)
- Reflectance and texture of real-world surfaces (1079 citations)
What are the main themes of his work throughout his whole career to date?
Shree K. Nayar mainly investigates Artificial intelligence, Computer vision, Computer graphics, Optics and Pixel.
His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Cognitive neuroscience of visual object recognition.
His Computer vision research includes elements of Lens and Brightness.
In his work, Epipolar geometry is strongly intertwined with Catadioptric system, which is a subfield of Computer graphics.
His work on Reflectivity, Photometric stereo and Specular reflection as part of his general Optics study is frequently connected to Photometry, thereby bridging the divide between different branches of science.
Shree K. Nayar focuses mostly in the field of Pixel, narrowing it down to topics relating to High dynamic range and, in certain cases, Image quality.
He most often published in these fields:
- Artificial intelligence (78.93%)
- Computer vision (72.59%)
- Computer graphics (24.11%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (78.93%)
- Computer vision (72.59%)
- Computer graphics (24.11%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Computer graphics, Image sensor and Optics.
Artificial intelligence is represented through his Image processing, Pixel, Structured light, Depth of field and Object research.
His Computer vision study combines topics from a wide range of disciplines, such as Lens and Aperture.
His research in Computer graphics intersects with topics in Motion and Pseudorandom number generator.
His work deals with themes such as Electronic circuit, Brightness, Flicker, Base and Signal, which intersect with Image sensor.
His work on Zoom and Target surface as part of general Optics study is frequently linked to Measurement method and Trap, therefore connecting diverse disciplines of science.
Between 2010 and 2021, his most popular works were:
- Describable Visual Attributes for Face Verification and Image Search (383 citations)
- PiCam: an ultra-thin high performance monolithic camera array (218 citations)
- Video from a single coded exposure photograph using a learned over-complete dictionary (178 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image sensor, Image processing and Computer graphics.
His Artificial intelligence research incorporates themes from Brightness and Aperture.
His research on Computer vision frequently connects to adjacent areas such as Lens.
His biological study spans a wide range of topics, including Camera auto-calibration and Light field.
Shree K. Nayar interconnects Deconvolution, Optics, Facial recognition system and Contextual image classification in the investigation of issues within Image processing.
His work investigates the relationship between Computer graphics and topics such as Image that intersect with problems in Bin and High-dynamic-range imaging.
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