Multiscale Classification using Complex Wavelets and Hidden Markov Tree Models

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Image Processingen_US
dc.citation.firstpage371en_US
dc.citation.lastpage374en_US
dc.citation.locationVancouver, Canadaen_US
dc.citation.volumeNumber2en_US
dc.contributor.authorRomberg, Justinen_US
dc.contributor.authorChoi, Hyeokhoen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorKingsbury, Nicholas G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:02:36Zen_US
dc.date.available2007-10-31T01:02:36Zen_US
dc.date.issued2000-09-01en_US
dc.date.modified2006-06-21en_US
dc.date.note2001-08-17en_US
dc.date.submitted2000-09-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractMultiresolution signal and image models such as the hidden Markov tree (HMT) aim to capture the statistical structure of smooth and singular (textured and edgy) regions. Unfortunately, models based on the orthogonalwavelet transform suffer from shift-variance, making them less accurate and realistic. In this paper, we extend the HMT modeling framework to the complex wavelet transform, which features near shift-invariance and improved angular resolution compared to the standard wavelet transform. The model is computationally efficient (featuring linear-time computation and processing algorithms) and applicable to general Bayesian inference problems as a prior density for the data. In this paper, we develop a simple multiscale maximum likelihood classification scheme based on the complex wavelet HMT that outperforms those based on traditional real-valued wavelet transforms. The resulting classification can be used as a front end in a more sophisticated multiscale segmentation algorithm.en_US
dc.identifier.citationJ. Romberg, H. Choi, R. G. Baraniuk and N. G. Kingsbury, "Multiscale Classification using Complex Wavelets and Hidden Markov Tree Models," vol. 2, 2000.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICIP.2000.899396en_US
dc.identifier.urihttps://hdl.handle.net/1911/20296en_US
dc.language.isoengen_US
dc.subjectcomplex waveletsen_US
dc.subjectmultiscaleen_US
dc.subjectclassificationen_US
dc.subject.keywordcomplex waveletsen_US
dc.subject.keywordmultiscaleen_US
dc.subject.keywordclassificationen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.titleMultiscale Classification using Complex Wavelets and Hidden Markov Tree Modelsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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