Occlusions and their relationship with the distribution of contrasts in natural images

An Information-Theory-like hypothesis recently proposed for early visual processing (the Minimal Local-Asperity hypothesis) accounts for the adaptive behavior with intensity of horizontal cells. It has been shown that for this to hold, the probability that a point is traversed by an occluding border must increase supralinearly (that is, with a positive second derivative) as a function of contrast. We test this condition by analyzing the distribution of contrasts and their relationship with occluding borders in natural images. We find that the distribution of contrasts in natural images falls exponentially as a function of contrast. Moreover, the probability that a point is traversed by an occluding border in natural images always rises with contrast until reaching one. This rise tends to be supralinear and addition of noise (at low intensities) increases the supralinearity, shifting the rising portion of the curve towards higher contrasts. These findings lend support to the Minimal Local-Asperity hypothesis, which proposes that one of the main roles of early retinal processing is to extract optimally edge, contrast, and luminance attributes from the visual world based on previous knowledge about natural images.

[1]  Song-Chun Zhu,et al.  Prior Learning and Gibbs Reaction-Diffusion , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  A. Ball,et al.  Background illumination reduces horizontal cell receptive-field size in both normal and 6-hydroxydopamine-lesioned goldfish retinas , 1991, Visual Neuroscience.

[3]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[4]  N M Grzywacz,et al.  The role of early retinal lateral inhibition: More than maximizing luminance information , 2000, Visual Neuroscience.

[5]  S. Bloomfield,et al.  Dark‐ and light‐induced changes in coupling between horizontal cells in mammalian retina , 1999, The Journal of comparative neurology.

[6]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[7]  S. Laughlin,et al.  Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  Jose L. Marroquin.  Regularization theory and low-level vision , 1998 .

[9]  B W Knight,et al.  The quantal source of area supralinearity of flash responses in Limulus photoreceptors , 1988, The Journal of general physiology.

[10]  G Westheimer,et al.  The spatial sense of the eye. Proctor lecture. , 1979, Investigative ophthalmology & visual science.

[11]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[12]  O. L. Zangwill,et al.  Current Problems in Animal Behavior , 1964 .

[13]  W. A. van de Grind,et al.  Spatial and temporal properties of cat horizontal cells after prolonged dark adaptation , 1996, Vision Research.

[14]  D. Baylor,et al.  Responses of retinal rods to single photons. , 1979, The Journal of physiology.

[15]  Joseph J. Atick,et al.  What Does the Retina Know about Natural Scenes? , 1992, Neural Computation.

[16]  M. Fuortes,et al.  Probability of Occurrence of Discrete Potential Waves in the Eye of Limulus , 1964, The Journal of general physiology.

[17]  Norberto M. Grzywacz,et al.  The Minimal Local-Asperity Hypothesis of Early Retinal Lateral Inhibition , 2000, Neural Computation.

[18]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[19]  S. McKee,et al.  Spatial configurations for visual hyperacuity , 1977, Vision Research.

[20]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[21]  Christopher W. Tyler,et al.  Occlusions contribute to scaling in natural images , 2001, Vision Research.

[22]  C J Dong,et al.  Cones contribute to light-evoked, dopamine-mediated uncoupling of horizontal cells in the mudpuppy retina. , 1994, Journal of neurophysiology.