Nonlinear analog networks for image smoothing and segmentation

Image smoothing and segmentation algorithms are frequently formulated as optimization problems. Linear and nonlinear (reciprocal) resistive networks have solutions characterized by an extremum principle. Thus, appropriately designed networks can automatically solve certain smoothing and segmentation problems in robot vision. Switched linear resistive networks and nonlinear resistive networks are considered for such tasks. Some fundamental theorems and simulation results are provided. >

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