Statistical signal processing usin wavelet-domain hidden markov models

Matthew S. Crouse; Robert D. Nowak; Richard G. Baraniuk

doi:10.1117/12.279689

Statistical signal processing usin wavelet-domain hidden markov models

Matthew S. Crouse, Robert D. Nowak, Richard G. Baraniuk

Houston Methodist

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

5 Scopus citations

Abstract

Most wavelet-based statistical signal and image processing techniques treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Pages	248-259
Number of pages	12
Volume	3169
DOIs	https://doi.org/10.1117/12.279689
State	Published - 1997
Event	Wavelet Applications in Signal and Image Processing V - San Diego, CA, United States Duration: Jul 30 1997 → Jul 30 1997

Other

Other	Wavelet Applications in Signal and Image Processing V
Country/Territory	United States
City	San Diego, CA
Period	7/30/97 → 7/30/97

Keywords

Estimation
Hidden markov models
Wavelets

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Condensed Matter Physics

Access to Document

10.1117/12.279689

Cite this

@inproceedings{ad8f21ec89624d07a490c1f99c4d97bd,

title = "Statistical signal processing usin wavelet-domain hidden markov models",

abstract = "Most wavelet-based statistical signal and image processing techniques treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.",

keywords = "Estimation, Hidden markov models, Wavelets",

author = "Crouse, \{Matthew S.\} and Nowak, \{Robert D.\} and Baraniuk, \{Richard G.\}",

year = "1997",

doi = "10.1117/12.279689",

language = "English (US)",

volume = "3169",

pages = "248--259",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "Wavelet Applications in Signal and Image Processing V ; Conference date: 30-07-1997 Through 30-07-1997",

}

TY - GEN

T1 - Statistical signal processing usin wavelet-domain hidden markov models

AU - Crouse, Matthew S.

AU - Nowak, Robert D.

AU - Baraniuk, Richard G.

PY - 1997

Y1 - 1997

N2 - Most wavelet-based statistical signal and image processing techniques treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.

AB - Most wavelet-based statistical signal and image processing techniques treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.

KW - Estimation

KW - Hidden markov models

KW - Wavelets

UR - https://www.scopus.com/pages/publications/0005499124

UR - https://www.scopus.com/inward/citedby.url?scp=0005499124&partnerID=8YFLogxK

U2 - 10.1117/12.279689

DO - 10.1117/12.279689

M3 - Conference contribution

AN - SCOPUS:0005499124

VL - 3169

SP - 248

EP - 259

BT - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Wavelet Applications in Signal and Image Processing V

Y2 - 30 July 1997 through 30 July 1997

ER -

Statistical signal processing usin wavelet-domain hidden markov models

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this