Markov Random Fields and Neural Networks with Applications to Early Vision Problems

Abstract The current resurgence of interest in Neural Networks has opened up several basic issues. In this chapter, we explore the connections between this area and Markov Random Fields. We are specifically concerned with early vision problems which have already benefited from a parallel and distributed computing perspective. We explore the relationships between the two fields at two different levels of a computational approach. Applications highlighting specific instances where ideas from the two approaches intertwine are discussed.

[1]  Federico Girosi,et al.  Parallel and deterministic algorithms from MRFs: surface reconstruction and integration , 1990, ECCV.

[2]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[3]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[4]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[5]  Rama Chellappa,et al.  Stochastic and deterministic networks for texture segmentation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[6]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  Kumpati S. Narendra,et al.  Learning automata - an introduction , 1989 .

[9]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[10]  Carsten Peterson,et al.  A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..

[11]  D. Howard.  Giles, and Y. , 1973 .

[12]  S. Lloyd,et al.  Complexity as thermodynamic depth , 1988 .

[13]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[14]  Tomaso Poggio,et al.  Probabilistic Solution of Ill-Posed Problems in Computational Vision , 1987 .

[15]  T Poggio,et al.  Parallel integration of vision modules. , 1988, Science.

[16]  Anand Rangarajan,et al.  Generalized graduated nonconvexity algorithm for maximum a posteriori image estimation , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[17]  Hermann Haken,et al.  Information and Self-Organization: A Macroscopic Approach to Complex Systems , 2010 .

[18]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[19]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[20]  J. Besag.  Efficiency of pseudolikelihood estimation for simple Gaussian fields , 1977 .

[21]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  C. Malsburg,et al.  Statistical Coding and Short-Term Synaptic Plasticity: A Scheme for Knowledge Representation in the Brain , 1986 .

[23]  David Marr,et al.  VISION A Computational Investigation into the Human Representation and Processing of Visual Information , 2009 .

[24]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[25]  C. Lee Giles,et al.  Nonlinear dynamics of artificial neural systems , 1987 .