Large calibrated datasets of'random'natural images have recently become available. These
make possible precise and intensive statistical studies of the local nature of images. We …

We describe a statistical model for natural images that is built upon a multi-scale wavelet
decomposition. The model consists of first-and higher-order statistics that capture certain …

The proliferation of low-cost infrared cameras gives us a new angle for attacking many
unsolved vision problems by leveraging a larger range of the electromagnetic spectrum. A …

We develop a scale-invariant version of Matheron's “dead leaves model” for the statistics of
natural images. The model takes occlusions into account and resembles the image …

The statistics of range images from natural environments is a largely unexplored field of
research. It closely relates to the statistical modeling of the scene geometry in natural …

Natural images are known to have scale invariant statistics. While some eariler studies have
reported the kurtosis of marginal bandpass filter response distributions to be constant …

Many low-level vision algorithms assume a prior probability over images, and there has
been great interest in trying to learn this prior from examples. Since images are very non …

We develop nonparametric Bayesian models for multiscale representations of images
depicting natural scene categories. Individual features or wavelet coefficients are marginally …

We present an unsupervised technique for visual learning which is based on density
estimation in high-dimensional spaces using an eigenspace decomposition. Two types of …

In this paper we propose and examine non-parametric statistical tests to define similarity and
homogeneity measures for textures. The statistical tests are applied to the coefficients of …