HMM-based multiresolution image segmentation

Jiuliu Lu, Lawrence Carin

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

12 Scopus citations


A texture segmentation algorithm is developed, utilizing a wavelet-based multi-resolution analysis of general imagery. The wavelet analysis yields a set of quadtrees, each composed of high-high (HH), high-low (HL) and low-high (LH) wavelet coefficients. Hidden Markov trees (HMTs) are designed for the quadtrees. For a given texture we define a set of "hidden" states, and a hidden Markov model (HMM) is developed to characterize the statistics of a given quadtree with respect to the statistics of surrounding quadtrees. Each HMM state is characterized by a unique set of HMTs. An HMM-HMT model is developed for each texture of interest, with which image segmentation is achieved. Several numerical examples are presented to demonstrate the model, with comparisons to alternative approaches.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
StatePublished - Jan 1 2002
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2021-02-09


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