Hierarchical wavelet-based image model for pattern analysis and synthesis

Clayton D. Scott; Robert D. Nowak

doi:10.1117/12.408602

4 December 2000 Hierarchical wavelet-based image model for pattern analysis and synthesis

Clayton D. Scott, Robert D. Nowak

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408602
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

Despite their success in other areas of statistical signal processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations 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 pattern transformations. 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, a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length complexity regularization to learn a template with a sparse representation in the wavelet domain. We illustrate template learning with examples, and discuss how TEMPLAR applies to pattern classification and denoising from multiple, unaligned observations.

Citation Download Citation

Clayton D. Scott and Robert D. Nowak "Hierarchical wavelet-based image model for pattern analysis and synthesis", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408602

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