DocumentCode :
3000956
Title :
Long correlation random field image models
Author :
Morgera, Salvatore D. ; Forbes, Zendal P.
Author_Institution :
Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada
fYear :
1988
fDate :
11-14 Apr 1988
Firstpage :
1036
Abstract :
Random field models have found applications in many areas including image analysis and processing. Techniques include the simultaneous (AR, MA, and SARMA) models and the conditional Markov (CM) models. To motivate the long correlation (LC) models examined, a brief review of both the SARMA and CM methods is provided. The LC random field models are then presented; these models are a generalization of SAR models and possess only a few parameters. Simulations are provided to illustrate the affect of parameter variation on the correlation, or texture, of the resulting random field. Very general experiments jointly employing SMA and LC models are also described; a combination of models of this type is seen to permit exceptionally flexible control over both low and high frequency random field behavior
Keywords :
correlation methods; picture processing; random processes; AR; CM; MA; SAR; SARMA; conditional Markov models; image analysis; image processing; long correlation random field image models; parameter variation; simultaneous models; texture; Autocorrelation; Eigenvalues and eigenfunctions; Fourier transforms; Frequency; Lattices; Statistics; Transfer functions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on
Conference_Location :
New York, NY
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.1988.196770
Filename :
196770
Link To Document :
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