What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
David C. Knill mainly investigates Artificial intelligence, Computer vision, Perception, Psychophysics and Optical illusion.
David C. Knill is involved in the study of Artificial intelligence that focuses on Bayes' theorem in particular.
His study in Bayes' theorem is interdisciplinary in nature, drawing from both Coding, Prior probability, Neural coding and Bayesian inference.
His work in the fields of Perceptual learning overlaps with other areas such as Anisometropia.
His Psychophysics study results in a more complete grasp of Neuroscience.
His studies deal with areas such as Orientation, Lightness, Luminance and Contrast as well as Optical illusion.
His most cited work include:
- The Bayesian brain: the role of uncertainty in neural coding and computation (1456 citations)
- Perception as Bayesian Inference (892 citations)
- Do humans optimally integrate stereo and texture information for judgments of surface slant (342 citations)
What are the main themes of his work throughout his whole career to date?
David C. Knill mainly focuses on Artificial intelligence, Computer vision, Perception, Communication and Optics.
The Artificial intelligence study combines topics in areas such as Depth perception and Pattern recognition.
His Motion, Orientation and Visual feedback study in the realm of Computer vision connects with subjects such as Optical flow.
Within one scientific family, David C. Knill focuses on topics pertaining to Information processing under Perception, and may sometimes address concerns connected to Scaling.
His Communication study incorporates themes from Stimulus, Sensory cue, Motor control and Eye movement.
The concepts of his Bayes' theorem study are interwoven with issues in Posterior probability, Psychophysics and Inference.
He most often published in these fields:
- Artificial intelligence (63.56%)
- Computer vision (44.07%)
- Perception (26.27%)
What were the highlights of his more recent work (between 2012-2016)?
- Artificial intelligence (63.56%)
- Computer vision (44.07%)
- Perception (26.27%)
In recent papers he was focusing on the following fields of study:
David C. Knill mostly deals with Artificial intelligence, Computer vision, Perception, Motion perception and Stereopsis.
He interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
His study in the fields of Object under the domain of Computer vision overlaps with other disciplines such as Interception.
David C. Knill has included themes like Illusion, Motion, Position and Communication in his Perception study.
David C. Knill studied Motion perception and Visual perception that intersect with Eye movement, Visual processing and Synesthesia.
His Inference research incorporates elements of Bayes' theorem and Set.
Between 2012 and 2016, his most popular works were:
- Stereopsis and amblyopia: A mini-review (153 citations)
- Unifying account of visual motion and position perception (65 citations)
- Recovering stereo vision by squashing virtual bugs in a virtual reality environment (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Stereoscopic acuity, Stereopsis, Computer vision and Perception.
David C. Knill specializes in Artificial intelligence, namely Motion perception.
His Stereoscopic acuity research integrates issues from Monocular, Virtual Reality Exposure Therapy and Stereoscopy.
His research integrates issues of Depth perception, Visual cortex, Perceptual learning and Developmental psychology in his study of Stereopsis.
His studies in Computer vision integrate themes in fields like Illusion, Visual perception and Position.
David C. Knill combines subjects such as Motion and Neuroplasticity with his study of Perception.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
