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dc.contributor.authorNeelamani, Ramesh
Romberg, Justin
Riedi, Rudolf H.
Choi, Hyeokho
Baraniuk, Richard G.
dc.creatorNeelamani, Ramesh
Romberg, Justin
Riedi, Rudolf H.
Choi, Hyeokho
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T00:55:23Z
dc.date.available 2007-10-31T00:55:23Z
dc.date.issued 2000-07-01
dc.date.submitted 2000-07-01
dc.identifier.citation R. Neelamani, J. Romberg, R. H. Riedi, H. Choi and R. G. Baraniuk, "Multiscale Image Segmentation Using Joint Texture and Shape Analysis," vol. 4119, 2000.
dc.identifier.urihttps://hdl.handle.net/1911/20138
dc.description Conference Paper
dc.description.abstract We develop a general framework to simultaneously exploit texture and shape characterization in multiscale image segmentation. By posing multiscale segmentation as a model selection problem, we invoke the powerful framework offered by minimum description length (MDL). This framework dictates that multiscale segmentation comprises multiscale texture characterization and multiscale shape coding. Analysis of current multiscale maximum a posteriori (MAP) segmentation algorithms reveals that these algorithms implicitly use a shape coder with the aim to estimate the optimal MDL solution, but find only an approximate solution. Towards achieving better segmentation estimates, we first propose a shape coding algorithm based on zero-trees which is well-suited to represent images with large homogeneous regions. For this coder, we design an efficient tree-based algorithm using dynamic programming that attains the optimal MDL segmentation estimate. To incorporate arbitrary shape coding techniques into segmentation, we design an iterative algorithm that uses dynamic programming for each iteration. Though the iterative algorithm is not guaranteed to attain exactly optimal estimates, it more effectively captures the prior set by the shape coder. Experiments demonstrate that the proposed algorithms yield excellent segmentation results on both synthetic and real world data examples.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjectSegmentation
texture
shape
minimum description length (MDL)
wavelets
hidden Markov trees (HMT)
dc.subject.otherImage Processing and Pattern analysis
Wavelet based Signal/Image Processing
Multiscale Methods
dc.title Multiscale Image Segmentation Using Joint Texture and Shape Analysis
dc.type Conference paper
dc.date.note 2001-09-04
dc.citation.bibtexName inproceedings
dc.date.modified 2006-06-26
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordSegmentation
texture
shape
minimum description length (MDL)
wavelets
hidden Markov trees (HMT)
dc.citation.volumeNumber 4119
dc.citation.location San Diego, CA
dc.citation.conferenceName Proc. SPIE, Wavelet Applications in Signal and Image Processign VIII
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1117/12.408607


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1321]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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