Parallel distributed networks for image smoothing and segmentation in analog VLSI

Consideration is given to switched linear resistive networks and nonlinear resistive networks for image smoothing and segmentation problems in robot vision. The latter network type is derived from the former by way of an intermediate stochastic formulation, and a new result relating the solution sets of the two is given for the so-called zero-temperature limit. The authors present simulation studies of several continuation methods that can be gracefully implemented in analog VLSI and that seem to given good results for these nonconvex optimization problems.<<ETX>>

[1]  Demetri Terzopoulos,et al.  Multigrid Relaxation Methods and the Analysis of Lightness, Shading and Flow , 1984 .

[2]  David B. Cooper,et al.  Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  J. Marroquín.  Optimal Bayesian Estimators For Image Segmentation and Surface Reconstruction , 1985 .

[4]  B..  Horn , 2022 .

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

[6]  Jin Luo,et al.  Resistive Fuses: Analog Hardware for Detecting Discontinuities in Early Vision , 1989, Analog VLSI Implementation of Neural Systems.

[7]  W. Millar.  CXVI. Some general theorems for non-linear systems possessing resistance , 1951 .

[8]  Tomaso Poggio,et al.  An Analog Model of Computation for the Ill-Posed Problems of Early Vision, , 1984 .

[9]  C Koch,et al.  Analog "neuronal" networks in early vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Jitendra Malik,et al.  A network for multiscale image segmentation , 1988, 1988., IEEE International Symposium on Circuits and Systems.

[11]  Berthold K. P. Horn.  Parallel networks for machine vision , 1991 .

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

[13]  Andrew Blake,et al.  Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  John J. Hopfield,et al.  Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

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