A hierarchical wavelet-based framework for pattern analysis and synthesis

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Title: A hierarchical wavelet-based framework for pattern analysis and synthesis
Author: Scott, Clayton Dean
Advisor: Nowak, Robert D.
Degree: Master of Science thesis
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 thesis 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 (MDL) complexity regularization to learn a template with a sparse representation in the wavelet domain. If we are given several trained models for different patterns, our framework provides a low-dimensional subspace classifier that is invariant to unknown pattern transformations as well as background clutter.
Citation: Scott, Clayton Dean. (2000) "A hierarchical wavelet-based framework for pattern analysis and synthesis." Masters Thesis, Rice University.
Date: 2000

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