Figures
Synthetic "dead-leaves" image
Natural images occupy a small subset of the high-dimensional manifold of possible images, and are therefore characterized by certain statistical properties. The image shown here is a synthetic "dead-leaves" image, which preserves many of the statistical structures common to natural images. Artificial neural networks were trained to recognize surfaces within these images under illumination that vary in space and time, with the result that the trained networks exhibited responses commensurate with human lightness illusions (see Corney and Lotto, e180).
Image Credit: Image by David Corney and R. Beau Lotto (http://www.lottolab.org)
Citation: (2007) PLoS Computational Biology Issue Image | Vol. 3(9) September 2007. PLoS Comput Biol 3(9): ev03.i09. https://doi.org/10.1371/image.pcbi.v03.i09
Published: September 28, 2007
Copyright: © 2007 Corney et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Natural images occupy a small subset of the high-dimensional manifold of possible images, and are therefore characterized by certain statistical properties. The image shown here is a synthetic "dead-leaves" image, which preserves many of the statistical structures common to natural images. Artificial neural networks were trained to recognize surfaces within these images under illumination that vary in space and time, with the result that the trained networks exhibited responses commensurate with human lightness illusions (see Corney and Lotto, e180).
Image Credit: Image by David Corney and R. Beau Lotto (http://www.lottolab.org)