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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
Type: Masters Thesis
Keywords: Wavelets; pattern analysis; MDL
Citation: C. Scott, "A Hierarchical Wavelet-Based Framework for Pattern Analysis and Synthesis," Masters Thesis, 2000.
Abstract: Despite their success in other areas of statsitical 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.
Date Published: 2000-04-20

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  • ECE Publications [1030 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.