Markov Random Fields : Theory and Application
This book introduces the theory and applications of Markov Random fields in image processing and computer vision. Modelling images through the local interaction of Markov models has resulted in useful algorithms for problems in texture analysis, image synthesis, image restoration, image segmentation, surface reconstruction and integration of low-level visual modules. All of the contributors are leading researchers from the United States and Europe. The book presents statistical modelling of two-and three-dimensional images; includes Markov random fields, Gibbs distribution, and simulated annealing explains integration or fusion of images; covers image segmentation, texture analysis, and image restoration using MRF models of context; gives a systematic development of algorithms for image processing, analysis, and computer vision and present parallel algorithms for image processing, analysis, and computer vision.
- Hardback | 672 pages
- 152.4 x 233.68 x 30.48mm | 907.18g
- 01 Apr 1993
- Elsevier Science Publishing Co Inc
- Academic Press Inc
- San Diego, United States
Table of contents
Image modeling during the 1980s - a brief overview, A. Rosenfeld; compound Gauss-Markov random fields for parallel image processing, J.W. Woods, et al; stochastic algorithms for restricted image spaces and experiments in deblurring, D. Geman, et al; a continuation method for image estimation using the A-diabatic approximation, A. Rangarahan and R. Chellappa; isotropic priors for single photon emission computed tomography, S. Geman; Gaussian Markov random fields at multiple resolution, S. Lakshmanan and H. Derin; texture synthesis and classification, S. Chatterjee; spectral estimation for random fields with applications to Markov Modelling and texture classification, J. Yuan and T.S. Rao; probabilistic network inference for cooperative high and low level vision, P.B. Chow, et al; stereo matching, S. Barnard; 3-D analysis of A shaded and textural surface image, R.L. Kashyap; shape from texture using Gaussian Markov random fields, F.S. Cohen and M. Patel; the use of Markov random fields in estimating and reorganizing objects in 3D spaces, D.B. Cooper, et al; A Markov random field model-based approach to image interpretation, J.W. Modestino and J. Zhang; A Markov random field restoration of image sequences, T.J. Hainsworth and K.V. Mardia; the MIT vision machine - progress in the integration of vision modules, T. Poggio and D. Weinshall; parameter estimation for Gibbs distributions from fully observed data, B. Gidas; on sampling methods and annealing algorithms, S.B. Gelfand and S. Mitter; adaptive Gibbsian automata, J.L. Marroquin and A. Ramirez.