We review a recent 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 {\it Bessel K
form}s, has been derived \cite{grenander-srivastava-clutter}. This
parametric family matches well the observed histograms for a large
variety of images (video, range, infrared, etc.) and filters
(Gabors, Laplacian Gaussian, derivatives, etc). The Bessel
parameters relate to the 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.