Convergence behavior and root signal sets of stack filters

The four different types of stack filters, type-0 through type-3, are determined by four different shapes of the on-set of the positive Boolean function from which the stack filter is constructed.Under all three appending strategies commonly considered in the literature-first and last value carry-on strategy, constant value carry-on strategy, and the circular approach-stack filters of type-0 through type-2 possess the convergence property, while type-3 stack filters do not all share this property. Examples of cyclic behavior in type-3 stack filters are given. Conditions under which certain operations on stack filters which possess the convergence property produce other filters with this property are provided.In perhaps the most important result in this paper, it is shown that the root signal set of any type-3 stack filter is the intersection of the root sets of the type-1 and type-2 stack filters from which the type-3 filter is constructed. This should simplify the task of finding the set of roots of type-3 stack filters.The rates of convergence for stack filters of type-1 and type-2 are determined for each appending approach. The convergence behavior and rates of convergence of stack filters of type-1 and type-2 are then generalized to include type-1 and type-2 filters with indexi.

[1]  Yrjö Neuvo,et al.  A New Class of Detail-Preserving Filters for Image Processing , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  S. G. Tyan,et al.  Median Filtering: Deterministic Properties , 1981 .

[3]  G. Wise,et al.  A theoretical analysis of the properties of median filters , 1981 .

[4]  Peter D. Wendt Nonrecursive and recursive stack filters and their filtering behavior , 1990, IEEE Trans. Acoust. Speech Signal Process..

[5]  Edward J. Coyle,et al.  Root properties and convergence rates of median filters , 1985, IEEE Trans. Acoust. Speech Signal Process..

[6]  Moncef Gabbouj,et al.  Optimal stack filtering and the estimation and structural approaches to image processing , 1989, Sixth Multidimensional Signal Processing Workshop,.

[7]  Peter Wendt Convergence properties of multidimensional stack filters , 1990, Other Conferences.

[8]  J. Fitch,et al.  Median filtering by threshold decomposition , 1984 .

[9]  Petros Maragos,et al.  Morphological filters-Part I: Their set-theoretic analysis and relations to linear shift-invariant filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[10]  Edward J. Coyle,et al.  Stack filters , 1986, IEEE Trans. Acoust. Speech Signal Process..

[11]  Edward J. Coyle,et al.  Some convergence properties of median filters , 1986 .

[12]  Gonzalo R. Arce,et al.  Detail-preserving ranked-order based filters for image processing , 1989, IEEE Trans. Acoust. Speech Signal Process..

[13]  Edward J. Coyle,et al.  Analysis and Implementation of Median Type Filters , 1984 .

[14]  N. Gallagher,et al.  Two-dimensional root structures and convergence properties of the separable median filter , 1983 .

[15]  Pao-Ta Yu,et al.  Convergence behavior and N-roots of stack filters , 1990, IEEE Trans. Acoust. Speech Signal Process..

[16]  Robert Bartle,et al.  The Elements of Real Analysis , 1977, The Mathematical Gazette.

[17]  Farzin Mokhtarian,et al.  Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Ingo Wegener,et al.  The complexity of Boolean functions , 1987 .

[19]  Moncef Gabbouj,et al.  Minimum Mean Absolute Error Stack Filtering with Structural Constraints and Goals , 1990 .