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Proceedings Paper

Using wavelets to learn pattern templates
Author(s): Clayton D. Scott; Robert D. Nowak
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Paper Abstract

Despite the success of wavelet decompositions in other areas of statistical signal and image processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations (e.g., translation, rotation, location of lighting source) inherent in most pattern observations. In this paper we introduce a hierarchical wavelet-based framework for modeling patterns in digital images. This framework takes advantage of the efficient image representations afforded by wavelets, while accounting for unknown translation and rotation. Given a trained model, we can use this framework to synthesize pattern observations. If the model parameters are unknown, we can infer them from labeled training data using TEMPLAR (Template Learning from Atomic Representations), a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length (MDL) complexity regularization to learn a template with a sparse representation in the wavelet domain. We discuss several applications, including template learning, pattern classification, and image registration.

Paper Details

Date Published: 25 July 2002
PDF: 12 pages
Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); doi: 10.1117/12.477035
Show Author Affiliations
Clayton D. Scott, Rice Univ. (United States)
Robert D. Nowak, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 4726:
Automatic Target Recognition XII
Firooz A. Sadjadi, Editor(s)

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