Texture synthesis via a noncausal nonparametric multiscale Markov random field

Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger r...

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Bibliographic Details
Published in:IEEE transactions on image processing 1998-06, Vol.7 (6), p.925-931
Main Authors: Paget, R., Longstaff, I.D.
Format: Article
Language:English
Subjects:
Annealing
Applied sciences
Artificial intelligence
Autoregressive processes
Computer science
control theory
systems
Exact sciences and technology
Fractals
Image processing
Image segmentation
Image texture analysis
Information, signal and communications theory
Markov random fields
Multidimensional systems
Pattern recognition. Digital image processing. Computational geometry
Sensor phenomena and characterization
Signal processing
Stochastic processes
Synthetic aperture radar
Telecommunications and information theory
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Summary:Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.
ISSN:1057-7149
1941-0042
DOI:10.1109/83.679446