Abstract

Recently, different models of the statistical structure of natural images have been proposed. These models predict properties of biological visual systems and can be used as priors in Bayesian inference. The fundamental model is independent component analysis, which can be estimated by maximization of the sparsenesses of linear filter outputs. This leads to the emergence of principal simple cell properties. Alternatively, simple cell properties are obtained by maximizing the temporal coherence in natural image sequences. Taking account of the basic dependencies of linear filter outputs permit modeling of complex cells and topographic organization as well. We propose a unifying framework for these statistical properties, based on the concept of spatiotemporal activity “bubbles.” A bubble means here an activation of simple cells (linear filters) that is contiguous both in space (the cortical surface) and in time.

© 2003 Optical Society of America

Surface segmentation based on the luminance and color statistics of natural scenes

Ione Fine, Donald I. A. MacLeod, and Geoffrey M. Boynton
J. Opt. Soc. Am. A 20(7) 1283-1291 (2003)

Statistical correlations between two-dimensional images and three-dimensional structures in natural scenes

Brian Potetz and Tai Sing Lee
J. Opt. Soc. Am. A 20(7) 1292-1303 (2003)

Factorial coding of natural images: how effective are linear models in removing higher-order dependencies?

Matthias Bethge
J. Opt. Soc. Am. A 23(6) 1253-1268 (2006)

Relations between the statistics of natural images and the response properties of cortical cells

David J. Field
J. Opt. Soc. Am. A 4(12) 2379-2394 (1987)

Probabilistic framework for the adaptation and comparison of image codes

Michael S. Lewicki and Bruno A. Olshausen
J. Opt. Soc. Am. A 16(7) 1587-1601 (1999)