Texture Generation from a Large Set of Registered Images Using Markov Random Fields

2012 ◽  
Author(s):  
Wilhelm Hannemann ◽  
Tilman Brock
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Large Set ◽  
Markov Random ◽  
Texture Generation

scholarly journals Julian Ernst Besag. 26 March 1945—6 August 2010

2018 ◽  
Vol 64 ◽  
pp. 27-50
Author(s):  
Peter J. Diggle ◽  
Peter J. Green ◽  
Bernard W. Silverman
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Lattice Models ◽  
Field Trials ◽  
Nearest Neighbour ◽  
Agricultural Field ◽  
Lattice Systems ◽  
Markov Random ◽  
Texture Generation

Julian Besag was an outstanding statistical scientist, distinguished for his pioneering work on the statistical theory and analysis of spatial processes, especially conditional lattice systems. His work has been seminal in statistical developments over the last several decades ranging from image analysis to Markov chain Monte Carlo methods. He clarified the role of auto-logistic and auto-normal models as instances of Markov random fields and paved the way for their use in diverse applications. Later work included investigations into the efficacy of nearest-neighbour models to accommodate spatial dependence in the analysis of data from agricultural field trials, image restoration from noisy data, and texture generation using lattice models.

Sampling from Gaussian Markov random fields conditioned on linear constraints

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Linear Constraints ◽  
Gaussian Markov Random Fields ◽  
Markov Random

scholarly journals A Methodology for Redesigning Networks by Using Markov Random Fields

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Random Fields ◽  
Markov Random Fields ◽  
Application Performance ◽  
Cross Border ◽  
Markov Random ◽  
User Groups ◽  
Universal Definition ◽  
Definition Of ◽  
Decision Making Model

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

Optimal smoothing for spherical Gauss–Markov Random Fields with application to weather data estimation

2017 ◽  
Vol 33 ◽  
pp. 43-51 ◽  
Author(s):  
Alessandro Borri ◽  
Francesco Carravetta ◽  
Langford B. White
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Weather Data ◽  
Markov Random ◽  
Optimal Smoothing ◽  
Gauss Markov Random Fields ◽  
Data Estimation
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