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<p><b>Abstract</b>—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 [11] and called <b>Bessel K forms</b>, 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 <tmath>\big. L^2\hbox{-}{\rm{metric}}\bigr.</tmath> on the set of Bessel K forms, we propose a pseudometric on the image space for quantifying image similarities/differences. Some applications, including clutter classification and pruning of hypotheses for target recognition, are presented.</p>
Image probabilities, spectral analysis, Bessel K forms, clutter classification, target recognition, Gabor filters.

A. Srivastava, X. Liu and U. Grenander, "Universal Analytical Forms for Modeling Image Probabilities," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 1200-1214, 2002.
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