Multiscale edge grammars for complex wavelet transforms

J. K. Romberg, H. Choi, R. G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


Wavelet domain algorithms have risen to the forefront of image processing. The power of these algorithms is derived from the fact that the wavelet transform restructures images in a way that makes statistical modeling simpler. Since edge singularities account for the most important information in images, understanding how edges behave in the wavelet domain is the key to modeling. In the past, wavelet-domain statistical models have codified the tendency for wavelet coefficients representing an edge to be large across scale. In this paper, we use the complex wavelet transform to uncover the phase behavior of wavelet coefficients representing an edge. This allows us to design a hidden Markov tree model that can discriminate between large magnitude wavelet coefficients caused by texture regions and ones caused by edges.

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Number of pages4
StatePublished - Jan 1 2001
EventIEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001


OtherIEEE International Conference on Image Processing (ICIP) 2001

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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