A universal hidden Markov tree image model
| dc.contributor.advisor | Baraniuk, Richard G. | en_US |
| dc.creator | Romberg, Justin K. | en_US |
| dc.date.accessioned | 2009-06-04T06:51:56Z | en_US |
| dc.date.available | 2009-06-04T06:51:56Z | en_US |
| dc.date.issued | 1999 | en_US |
| dc.description.abstract | Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training. We propose two reduced-parameter HMT models that capture the general structure of a broad class of real-world images. In the image HMT model, we use the fact that for real-world images the structure of the HMT is self-similar across scale, allowing us to reduce the complexity of the model to just nine parameters. In the universal HMT we fix these nine parameters, eliminating training while retaining nearly all of the key structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms all other wavelet-based estimators in the current literature. | en_US |
| dc.format.extent | 43 p. | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.callno | THESIS E.E. 1999 ROMBERG | en_US |
| dc.identifier.citation | Romberg, Justin K.. "A universal hidden Markov tree image model." (1999) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/17293">https://hdl.handle.net/1911/17293</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/17293 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | System science | en_US |
| dc.title | A universal hidden Markov tree image model | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |
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