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