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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wendt, P. |
| Copyright Year | 1994 |
| Description | Author affiliation: IBM Corp., Austin, TX, USA (Wendt, P.) |
| Abstract | Stack filters are a large class of nonlinear filters that include median and ranked-order filters. Usually, these filters are analyzed in terms of their invariant signals and their convergence behavior. Convergence refers to whether iterative application of a stack filter to a signal will make it converge to an invariant signal. Previous convergence results have been deterministic results for limited classes of stack filters. In these results, the "visiting strategy" for the filter kernel as it filters a signal is arbitrary, but known a priori. Here, we use a new stochastic approach, with randomly evolving visiting strategies, that applies to all stack filters. We show that, within this framework, all stack filters make all input signals converge almost surely. A result for Boolean networks follows as a corollary. |
| Starting Page | 800 |
| Ending Page | 804 |
| File Size | 413401 |
| Page Count | 5 |
| File Format | |
| ISBN | 0818669527 |
| DOI | 10.1109/ICIP.1994.413425 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-11-13 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Stochastic processes Convergence Neural networks Kernel Limit-cycles Nonlinear filters |
| Content Type | Text |
| Resource Type | Article |
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