What is he best known for?
The fields of study he is best known for:
- Computer graphics
- Operating system
- Artificial intelligence
His primary scientific interests are in Computer graphics, Graphics, Pixel, Artificial intelligence and Computer vision.
His research integrates issues of Set and Virtual reality in his study of Computer graphics.
His Graphics research includes elements of Shading and Texture mapping.
His Pixel study combines topics from a wide range of disciplines, such as Display device, Computer hardware and Sample.
His work on Stereo display as part of general Artificial intelligence research is frequently linked to Hot spot, thereby connecting diverse disciplines of science.
In general Computer vision study, his work on Eye tracking often relates to the realm of Geometry compression, thereby connecting several areas of interest.
His most cited work include:
- Geometry compression (659 citations)
- The triangle processor and normal vector shader: a VLSI system for high performance graphics (274 citations)
- High resolution virtual reality (233 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Graphics, Computer hardware, Computer graphics, Pixel and Sample.
His work carried out in the field of Graphics brings together such families of science as Computer graphics and Rendering.
His study in Computer hardware is interdisciplinary in nature, drawing from both Floating point, Controller, Set and Transfer.
His Floating point research is multidisciplinary, relying on both Huffman coding, Block, Graphics pipeline and Geometry.
Michael F. Deering combines subjects such as Computer vision and Artificial intelligence with his study of Computer graphics.
His Pixel research incorporates elements of Filter and Display device.
He most often published in these fields:
- Graphics (37.42%)
- Computer hardware (36.20%)
- Computer graphics (35.58%)
What were the highlights of his more recent work (between 2000-2009)?
- Graphics (37.42%)
- Sample (18.40%)
- Pixel (27.61%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Graphics, Sample, Pixel, Computer graphics and Computer hardware.
His work deals with themes such as Rendering and Parallel computing, which intersect with Graphics.
The various areas that he examines in his Sample study include Algorithm, Computation, Set and Artificial intelligence.
His research in Pixel intersects with topics in Filter, Opacity, Refresh cycle and Display device.
His research in the fields of Render Target, Raster graphics and Texture memory overlaps with other disciplines such as SPHERES.
His Computer hardware research incorporates themes from Interface, Graphics pipeline, Controller, Real-time computing and Scheduling.
Between 2000 and 2009, his most popular works were:
- Scalable high performance 3D graphics (199 citations)
- Matching the edges of multiple overlapping screen images (111 citations)
- Graphics system configured to parallel-process graphics data using multiple pipelines (83 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer graphics
- Artificial intelligence
His primary areas of study are Pixel, Graphics, Sample, Artificial intelligence and Computer vision.
His studies deal with areas such as Computer graphics, Retina, Cornea, Diffraction and Rendering as well as Pixel.
Graphics is a subfield of Computer graphics that he investigates.
Michael F. Deering usually deals with Sample and limits it to topics linked to Filter and Normalization, Spatial cutoff frequency, Eye tracking and Depth of field.
Within one scientific family, Michael F. Deering focuses on topics pertaining to Display device under Artificial intelligence, and may sometimes address concerns connected to Brightness, Dot pitch and Wavefront.
His RGB color model study in the realm of Computer vision connects with subjects such as High color, Color space and Color correction.
This overview was generated by a machine learning system which analysed the scientist’s
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