Scalable data parallel algorithms for texture synthesis using Gibbs random fields

This article introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov random field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinki...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 4; no. 10; pp. 1456 - 1460
Main Authors: Bader, D.A., JaJa, J., Chellappa, R.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.10.1995
Institute of Electrical and Electronics Engineers
Subjects:
Applied sciences
Exact sciences and technology
Image coding
Image processing
Image restoration
Information, signal and communications theory
Least squares approximation
Noise shaping
Optimized production technology
Parallel algorithms
Signal processing
Signal processing algorithms
Signal restoration
Signal to noise ratio
Telecommunications and information theory
Random field
Texture analysis
Parameter estimation
Synthesis
Image processing
Maximum likelihood
Markov model
ISSN:1057-7149
Online Access:Get full text
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Description
Summary:This article introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov random field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. The use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented enables machine-independent scalable algorithms for a number of problems in image processing and analysis.< >
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ISSN:1057-7149
DOI:10.1109/83.465111