BAYESIAN IMAGING USING GOOD'S ROUGHNESS MEASURE - IMPLEMENTATION ON A MASSIVELY PARALLEL PROCESSOR.

Badrinath Roysam; Jay A. Shrauner; Michael I. Miller

BAYESIAN IMAGING USING GOOD'S ROUGHNESS MEASURE - IMPLEMENTATION ON A MASSIVELY PARALLEL PROCESSOR.

Badrinath Roysam, Jay A. Shrauner, Michael I. Miller

Research output: Contribution to journal › Conference article › peer-review

Abstract

A constrained maximum-likelihood estimator is derived by incorporating a rotationally invariant roughness penalty proposed by I. J. Good (1981) into the likelihood functional. This leads to a set of nonlinear differential equations the solution of which is a spline-smoothing of the data. The nonlinear partial differential equations are mapped onto a grid via finite differences, and it is shown that the resulting computations possess a high degree of parallelism as well as locality in the data-passage, which allows an efficient implementation on a 48-by-48 mesh-connected array of NCR GAPP processors. The smooth reconstruction of the intensity functions of Poisson point processes is demonstrated in two dimensions.

Original language	English (US)
Pages (from-to)	932-935
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
State	Published - 1988
Externally published	Yes

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Cite this

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title = "BAYESIAN IMAGING USING GOOD'S ROUGHNESS MEASURE - IMPLEMENTATION ON A MASSIVELY PARALLEL PROCESSOR.",

abstract = "A constrained maximum-likelihood estimator is derived by incorporating a rotationally invariant roughness penalty proposed by I. J. Good (1981) into the likelihood functional. This leads to a set of nonlinear differential equations the solution of which is a spline-smoothing of the data. The nonlinear partial differential equations are mapped onto a grid via finite differences, and it is shown that the resulting computations possess a high degree of parallelism as well as locality in the data-passage, which allows an efficient implementation on a 48-by-48 mesh-connected array of NCR GAPP processors. The smooth reconstruction of the intensity functions of Poisson point processes is demonstrated in two dimensions.",

author = "Badrinath Roysam and Shrauner, {Jay A.} and Miller, {Michael I.}",

year = "1988",

language = "English (US)",

pages = "932--935",

journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "0736-7791",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - BAYESIAN IMAGING USING GOOD'S ROUGHNESS MEASURE - IMPLEMENTATION ON A MASSIVELY PARALLEL PROCESSOR.

AU - Roysam, Badrinath

AU - Shrauner, Jay A.

AU - Miller, Michael I.

PY - 1988

Y1 - 1988

N2 - A constrained maximum-likelihood estimator is derived by incorporating a rotationally invariant roughness penalty proposed by I. J. Good (1981) into the likelihood functional. This leads to a set of nonlinear differential equations the solution of which is a spline-smoothing of the data. The nonlinear partial differential equations are mapped onto a grid via finite differences, and it is shown that the resulting computations possess a high degree of parallelism as well as locality in the data-passage, which allows an efficient implementation on a 48-by-48 mesh-connected array of NCR GAPP processors. The smooth reconstruction of the intensity functions of Poisson point processes is demonstrated in two dimensions.

AB - A constrained maximum-likelihood estimator is derived by incorporating a rotationally invariant roughness penalty proposed by I. J. Good (1981) into the likelihood functional. This leads to a set of nonlinear differential equations the solution of which is a spline-smoothing of the data. The nonlinear partial differential equations are mapped onto a grid via finite differences, and it is shown that the resulting computations possess a high degree of parallelism as well as locality in the data-passage, which allows an efficient implementation on a 48-by-48 mesh-connected array of NCR GAPP processors. The smooth reconstruction of the intensity functions of Poisson point processes is demonstrated in two dimensions.

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AN - SCOPUS:0023745554

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

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

ER -

BAYESIAN IMAGING USING GOOD'S ROUGHNESS MEASURE - IMPLEMENTATION ON A MASSIVELY PARALLEL PROCESSOR.

Abstract

ASJC Scopus subject areas

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