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dc.contributor.authorNdili, Unoma
Nowak, Robert David
Figueiredo, Mario
dc.creatorNdili, Unoma
Nowak, Robert David
Figueiredo, Mario
dc.date.accessioned 2007-10-31T00:55:05Z
dc.date.available 2007-10-31T00:55:05Z
dc.date.issued 2001-10-20
dc.date.submitted 2001-10-20
dc.identifier.citation U. Ndili, R. D. Nowak and M. Figueiredo, "Coding Theoretic Approach to Image Segmentation," 2001.
dc.identifier.urihttps://hdl.handle.net/1911/20133
dc.description Conference paper
dc.description.abstract In this paper, using a coding theoretic approach, we implement Rissanen's concept of minimum description length (MDL) for segmenting an image into piecewise homogeneous regions. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant across the image. The image pixels are (conditionally) independent and Gaussian, given the mean and variance functions. The model is intended to capture variations in both intensity (mean value) and texture (variance). We adopt a multi-scale tree based approach to develop two segmentation algorithms, using MDL to penalize overly complex segmentations. One algorithm is based on an adaptive (greedy) rectangular partitioning scheme. The second algorithm is an optimally-pruned wedgelet decorated dyadic partitioning. We compare the two schemes with an alternative constant variance dyadic CART (classification and regression tree) scheme which accounts only for variations in mean, and demonstrate their performance with SAR image segmentation problems.
dc.language.iso eng
dc.subjectimage segmentation
multiscale
SAR
wedgelets
dc.subject.otherImage Processing and Pattern analysis
Remote Sensing Applications
dc.title Coding Theoretic Approach to Image Segmentation
dc.type Conference paper
dc.date.note 2002-05-21
dc.citation.bibtexName inproceedings
dc.date.modified 2002-05-21
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordimage segmentation
multiscale
SAR
wedgelets
dc.citation.conferenceName IEEE International Conference on Image Processing
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1109/ICIP.2001.958055


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1289]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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