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Nonlinear Analog Networks for Image Smoothing and Segmentation

Author(s)
Lumsdaine, A.; Wyatt, J.L., Jr.; Elfadel, I.M.
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Abstract
Image smoothing and segmentation algorithms are frequently formulatedsas optimization problems. Linear and nonlinear (reciprocal) resistivesnetworks have solutions characterized by an extremum principle. Thus,sappropriately designed networks can automatically solve certainssmoothing and segmentation problems in robot vision. This papersconsiders switched linear resistive networks and nonlinear resistivesnetworks for such tasks. The latter network type is derived from thesformer via an intermediate stochastic formulation, and a new resultsrelating the solution sets of the two is given for the "zerostermperature'' limit. We then present simulation studies of severalscontinuation methods that can be gracefully implemented in analog VLSIsand that seem to give "good'' results for these non-convexsoptimization problems.
Date issued
1991-01-01
URI
http://hdl.handle.net/1721.1/5983
Other identifiers
AIM-1280
Series/Report no.
AIM-1280
Keywords
VLSI, graduated nonconvexity, analog networks, resistivesfuses, resistive grids, smoothing and segmentation

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