@string{SP = "Signal Processing"}
@string{NC = "Neural Computation"}

@article{Ade1983,
	author =	{F. Ade},
	title =		{Characterisation of textures by ``eigenfilters''},
	journal =	SP,
	volume =	{5},
	pages =		{451--457},
	year =		{1983}
}

@book{AaK1989,
	author =	{E. Aarts and J. Korst},
	title =		{Simulated annealing and {B}oltzmann machines},
	publisher =	{John Wiley and Sons},
	year =		{1989},
	address =	{New York}
}

@article{AGR1996,
	author =	{Joseph J. Atick and Paul A. Griffin and Redlich, A. Norman},
	title =		{The vocabulary of shape: principal shapes for probing
	perception and neural response},
	journal =	NC,
	volume =	{7},
	number =	{1},
	pages =		{1--5},
	month =		feb,
	year =		{1996},
	keywords =	{principal components, shape perception, eigenmode, eigenhead},
	abstract =	{Humans perceive shape rapidly and effortlessly but have
	great difficulties describing what they perceive. This suggests that
	the representation of shape in the brain is abstract and very unlike
	that used in conscious thought. Here we explore the proposal that this
	representation is matched to the statistical properties of objects in
	the environment. From an ensemble of several hundred laser-scanned
	three-dimensional (3D) human heads we extract the principal components
	which provide a compact basis for head shape. We show that, with good
	accuracy, a given head can be represented by linear combination of a
	few dozen primary shapes just as colours can be synthesized by
	combining the three principal colours. We suggest new perceptual
	adaptation experiments for testing the brain's shape representation
	system. The principal head shapes can also be used to probe response
	properties of `face-cells' in the inferior temporal cortex.}
}

@misc{WiS1994,
	author =	{Peter Williams and Thorsten Schnier},
	title =		{The {H}arvard Family of Bibliography Styles},
	howpublished =	{\LaTeX2e package documentation},
	month =		jun # {~21},
	year =		{1994},
	url =		{http://www.ctan.org/tex-archive/macros/latex/contrib/harvard/},
	note =		{(last accessed February 7, 2004)},
}

@misc{Dal1999,
	author =	{Patrick W. Daly},
	title =		{Natural Sciences Citations and References (Author--Year
	and Numerical Schemes)},
	howpublished =	{\LaTeX2e package documentation},
	month =		may # {~28},
	year =		{1999},
	url =		{http://www.ctan.org/tex-archive/macros/latex/contrib/natbib/},
	note =		{(last accessed February 7, 2004)},
}

@inproceedings{DeV1998,
	vgproject =	{cbir},
	author =	{De~Bonet, Jeremy S. and Paul Viola},
	title =		{Texture Recognition Using a Non-parametric Multi-Scale
	Statistical Model},
	crossref =	{CVPR98},
	year =		{1998},
	url =		{http://www.ai.mit.edu/\~{}jsd/research/publications/1998/DeBonet-CVPR98.pdf},
	abstract =	{We describe a technique for using the joint occurrence of
	local features at multiple resolutions to measure the similarity
	between texture images. Though superficially similar to a number of
	``Gabor'' style techniques, which recognize textures through the
	extraction of multi-scale feature vectors, our approach is derived from
	an accurate generative model of texture, which is explicitly
	multi-scale and non-parametric. The resulting recognition procedure is
	similarly non-parametric, and can model complex non-homogeneous
	textures. We report results on publicly available texture databases. In
	addition, experiments indicate that this approach may have sufficient
	discrimination power to perform target detection in synthetic aperture
	radar images (SAR).}
}

@proceedings{CVPR98,
	title =	{Proceedings of the 1998 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'98)},
	booktitle =	{Proceedings of the 1998 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'98)},
	key =		{CVPR98},
	month =		jun,
	year =		{1998},
	address =	{Santa Barbara, California, USA}
}