Stochastic models for capturing image variability

We review a result in modeling lower order (univariate and bivariate) probability densities of pixel values resulting from bandpass filtering of images. Assuming an object-based model for images, a parametric family of probabilities, called Bessel K forms, has been derived (Grenander and Srivastava 2001). This parametric family matches well with the observed histograms for a large variety of images (video, range, infrared, etc.) and filters (Gabor, Laplacian Gaussian, derivatives, etc). The Bessel parameters relate to certain characteristics of objects present in an image and provide fast tools either for object recognition directly or for an intermediate (pruning) step of a larger recognition system. Examples are presented to illustrate the estimation of Bessel forms and their applications in clutter classification and object recognition.