Random Fields Estimation Theory
Author: Alexander G. Ramm
Publisher:
Published: 2002
Total Pages: 273
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Alexander G. Ramm
Publisher:
Published: 2002
Total Pages: 273
ISBN-13:
DOWNLOAD EBOOKAuthor: Alexander G. Ramm
Publisher: World Scientific
Published: 2005
Total Pages: 390
ISBN-13: 9812565361
DOWNLOAD EBOOKThis book contains a novel theory of random fields estimation of Wiener type, developed originally by the author and presented here. No assumption about the Gaussian or Markovian nature of the fields are made. The theory, constructed entirely within the framework of covariance theory, is based on a detailed analytical study of a new class of multidimensional integral equations basic in estimation theory.This book is suitable for graduate courses in random fields estimation. It can also be used in courses in functional analysis, numerical analysis, integral equations, and scattering theory.
Author: Alexander G. Ramm
Publisher:
Published: 1990
Total Pages: 282
ISBN-13: 9780608052397
DOWNLOAD EBOOKAuthor: Havard Rue
Publisher: CRC Press
Published: 2005-02-18
Total Pages: 280
ISBN-13: 0203492021
DOWNLOAD EBOOKGaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
Author: Rama Chellappa
Publisher:
Published: 1993
Total Pages: 608
ISBN-13:
DOWNLOAD EBOOKIntroduces the theory and application of Markov random fields in image processing/computer vision. Modelling images through the local interaction of Markov models produces algorithms for use in texture analysis, image synthesis, restoration, segmentation and surface reconstruction.
Author: Dionissios T. Hristopulos
Publisher: Springer Nature
Published: 2020-02-17
Total Pages: 884
ISBN-13: 9402419187
DOWNLOAD EBOOKThis book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis. The contents of the book include topics from classical statistics and random field theory (regression models, Gaussian random fields, stationarity, correlation functions) spatial statistics (variogram estimation, model inference, kriging-based prediction) and statistical physics (fractals, Ising model, simulated annealing, maximum entropy, functional integral representations, perturbation and variational methods). The book also explores links between random fields, Gaussian processes and neural networks used in machine learning. Connections with applied mathematics are highlighted by means of models based on stochastic partial differential equations. An interlude on autoregressive time series provides useful lower-dimensional analogies and a connection with the classical linear harmonic oscillator. Other chapters focus on non-Gaussian random fields and stochastic simulation methods. The book also presents results based on the author’s research on Spartan random fields that were inspired by statistical field theories originating in physics. The equivalence of the one-dimensional Spartan random field model with the classical, linear, damped harmonic oscillator driven by white noise is highlighted. Ideas with potentially significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description of the Karhunen-Loève expansion of the Spartan model. The book will appeal to engineers, physicists, and geoscientists whose research involves spatial models or spatial data analysis. Anyone with background in probability and statistics can read at least parts of the book. Some chapters will be easier to understand by readers familiar with differential equations and Fourier transforms.
Author: Wolfgang Näther
Publisher:
Published: 1985
Total Pages: 192
ISBN-13:
DOWNLOAD EBOOKAuthor: Johanna Frieda Stoeckler
Publisher:
Published: 1999
Total Pages: 184
ISBN-13:
DOWNLOAD EBOOKAuthor: M. Sami Fadali
Publisher: Springer Nature
Published:
Total Pages: 489
ISBN-13: 9819980631
DOWNLOAD EBOOKAuthor: Stan Z. Li
Publisher: Springer Science & Business Media
Published: 2009-04-03
Total Pages: 372
ISBN-13: 1848002793
DOWNLOAD EBOOKMarkov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.