Nonlinear analog networks for image smoothing and segmentation

A. Lumsdaine, J. L. Wyatt, I. M. Elfadel

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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. This paper considers switched linear resistive networks and nonlinear resistive networks for such tasks. Following [1] the latter network type is derived from the former via an intermediate stochastic formulation, and a new result relating the solution sets of the two is given for the "zero temperature" limit. We then present simulation studies of several continuation methods that can be gracefully implemented in analog VLSI and that seem to give "good" results for these nonconvex optimization problems.

Original languageBritish English
Pages (from-to)53-68
Number of pages16
JournalJournal of VLSI Signal Processing
Issue number1-2
StatePublished - Jun 1991


Dive into the research topics of 'Nonlinear analog networks for image smoothing and segmentation'. Together they form a unique fingerprint.

Cite this