For instance, Yuille and Bülthoff [44] describe the Bayesian approach to perception in terms of faithful depiction: “We define vision as perceptual inference, the estimation of scene properties from an image or sequence of images . . . ther…the Rear-Mirror: Romantic Aesthetics, Culture, and Science Seen from Today. Festschrift for Frederick Burwick on the Occasion of His Seventieth Birthday, ed. W. Pape (WVT, Wissenschaftlicher Verlag Trier: Trier) (Studien zur Englischen Roma…
Steve Lehar
scientist · 2 mentions across 1 reading
In this course
Steve Lehar develops computational theories of visual perception that model how the brain constructs three-dimensional space from two-dimensional retinal images, positioning perception not as passive reception but as active inference and construction. The course readings appear to reference his work alongside Bayesian approaches to vision, suggesting his models are cited to illustrate how perception involves the brain's hypothetical reconstruction of scene properties rather than simple image capture. His framework becomes relevant for understanding how artificial vision systems and human visual cognition both engage in similar inferential processes, a key concern for the course's exploration of machine perception and its philosophical implications.
Mentioned in 1 reading
Appears alongside
People mentioned in the same passages — sorted by co-occurrence weight.
Alva Noë 1D. Heyer 1Dale Purves 1Donald D. Hoffman 1Evan Thompson 1Frederick Burwick 1J.K. Regan 1K.A. Jameson 1Louis Narens 1Martin A. Nowak 1Michele Mosca 1Natalia L. Komarova 1P. Kaye 1R. B. Lotto 1Rainer Mausfeld 1Raymond Laflamme 1Stephen E. Palmer 1Steven Pinker 1Vilayanur S. Ramachandran 1W. Pape 1Alan Yuille 1Heinrich Bülthoff 1Jeffrey Diehl 1Wilson S. Geisler 1