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Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience (1995)
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| Venue: | Neural Computation |
| Citations: | 209 - 13 self |
BibTeX
@INPROCEEDINGS{Williams95stochasticcompletion,
author = {Lance R. Williams and David W. Jacobs},
title = {Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience},
booktitle = {Neural Computation},
year = {1995},
pages = {837--858}
}
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Abstract
We describe an algorithm and representation level theory of illusory contour shape and salience. Unlike previous theories, our model is derived from a single assumption--- namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose points are positions and orientations in the image plane (i.e., the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). Our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. We show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory co...
Keyphrases
illusory contour shape random walk neural model stochastic completion field vector-field convolution previous theory representation level theory boundary completion shape boundary fragment single assumption illusory co image plane prior probability distribution maximum likelihood path explicit minimization mammalian visual cortex numerical relaxation
