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Invariant random boolean fields
Mathematical Notes of the Academy of Sciences of the USSR, 1969In the set of finite binary sequences a Markov process is defined with discrete time in which each symbol of the binary sequence at time t+1 depends on the two neighboring symbols at time t. A proof is given of the existence and uniqueness of an invariant distribution, and its derivation is also given in a number of cases.
Belyaev, Yu. K. +2 more
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Structures in random fields: Gaussian fields
Physical Review A, 1992We present two alternative methods for evaluating the probability densities of structures defined by d degrees of freedom in random fields. For Gaussian random fields, both differentiable and nondifferentiable, the application of these methods is considered in detail.
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Advances in Applied Probability, 1978
is of great importance in many applications. For example, if we consider a geographical map and denote height by X(t) where t is the set of geographical coordinates, Z(S) is the height of the highest mountain in the area S. In general, it is not possible to make any exact useful statements about the distribution of Z(S), and one must have recourse to ...
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is of great importance in many applications. For example, if we consider a geographical map and denote height by X(t) where t is the set of geographical coordinates, Z(S) is the height of the highest mountain in the area S. In general, it is not possible to make any exact useful statements about the distribution of Z(S), and one must have recourse to ...
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2008 International Machine Vision and Image Processing Conference, 2008
In this talk the author will outline some of the recent work undertaken by the Oxford Brookes Vision Group, a common theme underlying much of the research is to cast vision problems in terms of combinatorial optimization which provides a rich a deep theory for understanding them, with many new and exciting results.
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In this talk the author will outline some of the recent work undertaken by the Oxford Brookes Vision Group, a common theme underlying much of the research is to cast vision problems in terms of combinatorial optimization which provides a rich a deep theory for understanding them, with many new and exciting results.
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Markov Random Field Modeling in Image Analysis
Computer Science Workbench, 2001S. Li
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Stratigraphic uncertainty modelling with random field approach
, 2020W. Gong +5 more
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Advances in Gaussian random field generation: a review
Computational Geosciences, 2019Yang Liu, Jingfa Li, Shuyu Sun, Bo Yu
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Random fields on random graphs
Advances in Applied Probability, 1992The distribution (1) used previously by the author to represent polymerisation of several types of unit also prescribes quite general statistics for a random field on a random graph. One has the integral expression (3) for its partition function, but the multiple complex form of the integral makes the nature of the expected saddlepoint evaluation in ...
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Change detection method for remote sensing images based on an improved Markov random field
Multimedia tools and applications, 2017Wei Gu, Zhihan Lv, M. Hao
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DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016H. Fu +4 more
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