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Markov Random Field Modeling in Image Analysis

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables;

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Stochastic Modeling for Medical Image Analysis

-Gibbs random fields of these signals and/or labels of regions supporting the goal objects in the lattice. This valuable resource presents;

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Image Analysis, Random Fields and Markov Chain Monte Carlo Methods

This book is concerned with a probabilistic approach for image analysis, mostly from the Bayesian point of view, and the important Markov;

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Random Motions in Markov and SemiMarkov Random Environments 2

This book is the second of two volumes on random motions in Markov and semi-Markov random environments. This second volume focuses on high;

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Guide to Medical Image Analysis: Methods and Algorithms

labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.;

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Markov Processes for Stochastic Modeling

, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology;

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Bayesian and grAphical Models for Biomedical Imaging

papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis;

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Mathematical Modeling of Random and Deterministic Phenomena

This book highlights mathematical research interests that appear in real life, such as the study and modeling of random and deterministic;

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Image Processing with MATLAB

random field (MRF)-based image segmentation, boundary and curvature analysis methods, and parametric and geometric deformable models. The final;

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Advanced Concepts in Image Segmentation

, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the crux of the modernization effort;

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Hybrid Random Fields

of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these;

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Gaussian Markov Random Fields

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to;

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Gaussian Markov Random Fields

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to;

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Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic;

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Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic;

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Hidden Markov Models In Finance

A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance;

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Stochastic Modeling of Scientific Data

models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing;

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Stochastic Modeling of Scientific Data

models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing;

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Stochastic Geometry for Image Analysis

This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process;

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Statistical Methods for Spatio-Temporal Systems

generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field;

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Statistical Methods Of Spatio-Temporal Systems

generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field;

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Modeling for Reliability Analysis

Markov modeling has long been accepted as a fundamental and powerful technique for the fault tolerance analysis of mission-critical;

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Multi-resolution Image Fusion in Remote Sensing

resolution and multi stage guided filters. It includes in depth analysis on degradation estimation, Gabor Prior and Markov Random Field (MRF) Prior;

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Image Processing and Analysis with Graphs

minimization and modeling of pixel-labeling problems with graph cuts and Markov Random Fields Image processing with graphs: targeted;

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Markov Decision Processes

development in the Markov decision process area, namely structural policy analysis, approximation modeling, multiple objectives and Markov games;

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Statistical Inference for Piecewise-deterministic Markov Processes

Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance;

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Analysis of Computer and Communication Networks

design options used to build efficient switching equipment. Topics covered include Markov chains and queuing analysis, traffic modeling;

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