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A hierarchical wavelet-based framework for pattern analysis and synthesis

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dc.contributor.advisor Nowak, Robert D.
dc.creator Scott, Clayton Dean
dc.date.accessioned 2009-06-04T06:49:03Z
dc.date.available 2009-06-04T06:49:03Z
dc.date.issued 2000
dc.identifier.uri http://hdl.handle.net/1911/17376
dc.description.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.
dc.format.extent 41 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Statistics
Engineering, Electronics and Electrical
dc.title A hierarchical wavelet-based framework for pattern analysis and synthesis
dc.type.genre Thesis
dc.type.material Text
thesis.degree.discipline Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Scott, Clayton Dean. (2000) "A hierarchical wavelet-based framework for pattern analysis and synthesis." Masters Thesis, Rice University. http://hdl.handle.net/1911/17376.

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