Stochastic analysis for the recursive median filter process

Vector probability measure functions (density function) for recursively median filtered signals are found when the underlying input binary sequences are either independent identically distributed (i.i.d.) or Markov chains. The results are parametric in the window size of the filter and in the probab...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on information theory 1988-07, Vol.34 (4), p.669-679
Main Authors: Arce, G.R., Gallagher, N.C.
Format: Article
Language:English
Subjects:
Application software
Applied sciences
Density functional theory
Density measurement
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering theory
Information filtering
Information filters
Information, signal and communications theory
Probability
Signal and communications theory
Signal processing
Signal, noise
Statistics
Stochastic processes
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Vector probability measure functions (density function) for recursively median filtered signals are found when the underlying input binary sequences are either independent identically distributed (i.i.d.) or Markov chains. The results are parametric in the window size of the filter and in the probability distribution of the input sequence. Using statistical threshold decomposition, the same results are found for discrete alphabet random sequences that are either i.i.d. or Markov chains. Some examples illustrating the efficacy of the recursive median filter relative to the nonrecursive implementation are presented. In particular, the breakdown probabilities are tabulated for both recursive and nonrecursive median filters.< >
ISSN:0018-9448
1557-9654
DOI:10.1109/18.9767