Experiencing and perceiving visual surfaces.

A theoretical framework is proposed to understand binocular visual surface perception based on the idea of a mobile observer sampling images from random vantage points in space. Application of the generic sampling principle indicates that the visual system acts as if it were viewing surface layouts from generic not accidental vantage points. Through the observer's experience of optical sampling, which can be characterized geometrically, the visual system makes associative connections between images and surfaces, passively internalizing the conditional probabilities of image sampling from surfaces. This in turn enables the visual system to determine which surface a given image most strongly indicates. Thus, visual surface perception can be considered as inverse ecological optics based on learning through ecological optics. As such, it is formally equivalent to a degenerate form of Bayesian inference where prior probabilities are neglected.

[1]  B. Julesz Binocular depth perception of computer-generated patterns , 1960 .

[2]  C. Blakemore,et al.  The neural mechanism of binocular depth discrimination , 1967, The Journal of physiology.

[3]  James A. Anderson,et al.  A simple neural network generating an interactive memory , 1972 .

[4]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[5]  F Metelli,et al.  The perception of transparency. , 1974, Scientific American.

[6]  H. V. Tuijl,et al.  A new visual illusion: Neonlike color spreading and complementary color induction between subjective contours , 1975 .

[7]  G. Poggio,et al.  Binocular interaction and depth sensitivity in striate and prestriate cortex of behaving rhesus monkey. , 1977, Journal of neurophysiology.

[8]  P. Holland,et al.  Behavioral Studies of Associative Learning in Animals , 1982 .

[9]  L. Spillmann,et al.  The Neon Color Effect in the Ehrenstein Illusion , 1981, Perception.

[10]  T. Poggio,et al.  The analysis of stereopsis. , 1984, Annual review of neuroscience.

[11]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[12]  P. Cavanagh,et al.  Subjective contours capture stereopsis , 1985, Nature.

[13]  Shinsuke Shimojo,et al.  Da vinci stereopsis: Depth and subjective occluding contours from unpaired image points , 1990, Vision Research.

[14]  V. Ramachandran,et al.  Transparency: Relation to Depth, Subjective Contours, Luminance, and Neon Color Spreading , 1990, Perception.

[15]  K Nakayama,et al.  Toward a neural understanding of visual surface representation. , 1990, Cold Spring Harbor symposia on quantitative biology.

[16]  H. Barlow Conditions for versatile learning, Helmholtz's unconscious inference, and the task of perception , 1990, Vision Research.

[17]  K. Nakayama,et al.  Amodal Representation of Occluded Surfaces: Role of Invisible Stimuli in Apparent Motion Correspondence , 1990, Perception.