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Analytical image models and their applications. (English) Zbl 1034.68674

Heyden, Anders (ed.) et al., Computer vision - ECCV 2002. 7th European conference, Copenhagen, Denmark, May 28–31, 2002. Proceedings. Part 1. Berlin: Springer (ISBN 3-540-43745-2). Lect. Notes Comput. Sci. 2350, 37-51 (2002).
Summary: In this paper, we study a family of analytical probability models for images within the spectral representation framework. First the input image is decomposed using a bank of filters, and probability models are imposed on the filter outputs (or spectral components). A two-parameter analytical form, called a Bessel \(K\) form, derived based on a generator model, is used to model the marginal probabilities of these spectral components. The Bessel \(K\) parameters can be estimated efficiently from the filtered images and extensive simulations using video, infrared, and range images have demonstrated Bessel \(K\) form’s fit to the observed histograms. The effectiveness of Bessel \(K\) forms is also demonstrated through texture modeling and synthesis. In contrast to numeric-based dimension reduction representations, which are derived purely based on numerical methods, the Bessel \(K\) representations are derived based on object representations and this enables us to establish relationships between the Bessel parameters and certain characteristics of the imaged objects. We have derived a pseudo-metric on the image space to quantify image similarities/differences using an analytical expression for \(L^{2}\)-metri on the set of Bessel \(K\) forms. We have applied the Bessel \(K\) representation to texture modeling and synthesis, clutter classification, pruning of hypotheses for object recognition, and object classification. Results show that Bessel \(K\) representation captures important image features, suggesting its role in building efficient image understanding paradigms and systems.
For the entire collection see [Zbl 0992.68526].

MSC:

68U99 Computing methodologies and applications
68T45 Machine vision and scene understanding
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