Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet

2002 ◽  
Author(s):  
T.M. Chin ◽  
A.J. Mariano
Keyword(s):  
Random Field ◽  
Kalman Filtering ◽  
Markov Random Field ◽  
Large Scale ◽  
Geophysical Flows ◽  
Markov Random

scholarly journals A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Guoying Liu ◽  
Pengwei Li ◽  
Yun Zhang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Large Scale ◽  
Field Model ◽  
Image Clustering ◽  
Texture Image ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Color Texture

This paper presents a variation of the fuzzy local information c-means clustering (FLICM) algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF) together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.

Fusion Moves for Markov Random Field Optimization

2010 ◽  
Vol 32 (8) ◽  
pp. 1392-1405 ◽  
Author(s):  
Victor Lempitsky ◽  
Carsten Rother ◽  
Stefan Roth ◽  
Andrew Blake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Markov Random
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