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.
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.
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.
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.
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The Bayesian brain: the role of uncertainty in neural coding and computation
David C. Knill;Alexandre Pouget.
Trends in Neurosciences (2004)
Perception as Bayesian Inference
David C. Knill;Whitman Richards.
Do humans optimally integrate stereo and texture information for judgments of surface slant
David C. Knill;Jeffrey A. Saunders.
Vision Research (2003)
Humans use continuous visual feedback from the hand to control fast reaching movements
Jeffrey A. Saunders;David C. Knill.
Experimental Brain Research (2003)
Visual Feedback Control of Hand Movements
Jeffrey A. Saunders;David C. Knill.
The Journal of Neuroscience (2004)
Stereopsis and amblyopia: A mini-review
Dennis M. Levi;David C. Knill;Daphne Bavelier.
Vision Research (2015)
APPARENT SURFACE CURVATURE AFFECTS LIGHTNESS PERCEPTION
David C. Knill;Daniel Kersten.
The perception of cast shadows
Pascal Mamassian;David C. Knill;Daniel Kersten.
Trends in Cognitive Sciences (1998)
Human discrimination of fractal images
David C. Knill;David Field;Daniel Kerstent.
Journal of The Optical Society of America A-optics Image Science and Vision (1990)
Moving Cast Shadows Induce Apparent Motion in Depth
Daniel Kersten;Pascal Mamassian;David C Knill.
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