| 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 |
PDF |
| 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. "A hierarchical wavelet-based framework for pattern analysis and synthesis." Masters Thesis, Rice University, ETD http://hdl.handle.net/1911/17376. |
