Seeking probability models for images, we employ a spectral
approach where the images are decomposed using bandpass filters,
and probability models are imposed on the filter outputs (also
called spectral components). We employ a (two-parameter) family of
probability densities, introduced in
\cite{grenander-srivastava-clutter} and called {\bf Bessel K
forms}, for modeling the marginal densities of the spectral
components, and demonstrate their fit to the observed histograms
for video, infrared, and range images. Motivated by object-based
models for image analysis, a relationship between the Bessel
parameters and the imaged objects is established. Using
$L^2$-metric on the set of Bessel K forms, we propose a
pseudo-metric on the image space for quantifying image
similarities/differences. Some applications, including clutter
classification and pruning of hypotheses for target recognition,
are presented.