HMM-based multiresolution image segmentation

Jiuliu Lu; Lawrence Carin

doi:10.1109/icassp.2002.5745373

HMM-based multiresolution image segmentation

Jiuliu Lu, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

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 language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
DOIs	https://doi.org/10.1109/icassp.2002.5745373
State	Published - Jan 1 2002
Externally published	Yes

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

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title = "HMM-based multiresolution image segmentation",

abstract = "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.",

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year = "2002",

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AU - Carin, Lawrence

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PY - 2002/1/1

Y1 - 2002/1/1

N2 - 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.

AB - 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.

UR - http://ieeexplore.ieee.org/document/5745373/

UR - http://www.scopus.com/inward/record.url?scp=0036288369&partnerID=8YFLogxK

U2 - 10.1109/icassp.2002.5745373

DO - 10.1109/icassp.2002.5745373

M3 - Conference contribution

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

HMM-based multiresolution image segmentation

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