Accession Number:

ADA453106

Title:

Morphological Filters. Part 2. Their Relations to Median, Order-Statistic, and Stack Filters

Descriptive Note:

Journal article

Corporate Author:

HARVARD UNIV CAMBRIDGE MA DIV OF APPLIED SCIENCES

Personal Author(s):

Report Date:

1987-03-24

Pagination or Media Count:

44.0

Abstract:

This paper extends the theory of median, order-statistic OS, and stack filters by using mathematical morphology to analyze them and by relating them to those morphological erosions, dilations, openings, closings, and open-closings that commute with thresholding. The max-min representation of OS filters is introduced by showing that any median or other OS filter is equal to a maximum of erosions moving local minima and also to a minimum of dilations moving local maxima. Thus, OS filters can be computed by a closed formula that involves a max-min on prespecified sets of numbers and no sorting. Stack filters are established as the class of filters that are composed exactly of a finite number of max-min operations. The kernels of median, OS, and stack filters are collections of input signals that uniquely represent these filters due to their translation-invariance. The max-min functional definitions of these nonlinear filters is shown to be equivalent to a maximum of erosions by minimal with respect to a signal ordering kernel elements, and also to a minimum of dilations by minimal kernel elements of dual filters. The representation of stack filters based on their minimal kernel elements is proven to be equivalent to their representation based on irreducible sum-of-products expressions of Boolean functions. It is also shown that median filtering and its iterations of any signal by convex 1-D windows is bounded below by openings and above by closings a signal is a root fixed point of the mediari iff it is a toot of both an opening and a closing the open-closing and dos-opening yield median roots in one pass, suppress impulse noise similarly to the median, can discriminate between positive and negative noise impulses, and are computationally less complex than the median. Some similar results are obtained for 2-D median filtering.

Subject Categories:

  • Numerical Mathematics
  • Statistics and Probability
  • Theoretical Mathematics
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE