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Analysis of Multiscale Texture Segmentation using Wavelet-Domain Hidden Markov Trees

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dc.contributor.author Choi, Hyeokho
Hendricks, Brent
Baraniuk, Richard G.
dc.creator Choi, Hyeokho
Hendricks, Brent
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T00:46:50Z
dc.date.available 2007-10-31T00:46:50Z
dc.date.issued 1999-10-01
dc.date.submitted 1999-10-01
dc.identifier.uri http://hdl.handle.net/1911/19950
dc.description Conference Paper
dc.description.abstract This paper describes a technique for estimating the Kullback-Leibler (KL) distance between two Hidden Markov Models (HMMs), and for measuring the quality of the estimator. It also provides some results based on applying the technique to wavelet domain Hidden Markov Tree (HMT) models used in image segmentation. The technique is easily applied, because in most situations the necessary tools (data generation and likelihood calculation) are already in place.
dc.subject texture segmentation
wavelet
hidden marlov trees
dc.subject.other Image Processing and Pattern analysis
Wavelet based Signal/Image Processing
dc.title Analysis of Multiscale Texture Segmentation using Wavelet-Domain Hidden Markov Trees
dc.type Conference Paper
dc.date.note 2001-10-10
dc.citation.bibtexName inproceedings
dc.date.modified 2006-06-21
dc.contributor.center Digital Signal Processing (http://dsp.rice.edu/)
dc.subject.keyword texture segmentation
wavelet
hidden marlov trees
dc.citation.volumeNumber 2
dc.citation.pageNumber 1287-1291
dc.citation.location Pacific Grove, CA
dc.citation.conferenceName Asilomar Conference on Signals, Systems, and Computers
dc.identifier.citation H. Choi, B. Hendricks and R. G. Baraniuk,"Analysis of Multiscale Texture Segmentation using Wavelet-Domain Hidden Markov Trees," in Asilomar Conference on Signals, Systems, and Computers,, pp. 1287-1291.
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  • ECE Publications [1028 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [512 items]
    Publications by Rice Faculty and graduate students in digital signal processing.