The algebraic basis of mathematical morphology. I. dilations and erosions
Computer Vision, Graphics, and Image Processing
Optimal mean-square N-observation digital morphological filters: i. optimal binary filters
CVGIP: Image Understanding
Optimal mean-square N-observation digital morphological filters: ii. optimal gray-scale filters
CVGIP: Image Understanding
Computational mathematical morphology
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Mean-absolute-error representation and optimization of computational-morphological filters
Graphical Models and Image Processing
Optimal stack filters under rank selection and structural constraints
Signal Processing
Signal Processing
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Pattern Recognition Theory in Nonlinear Signal Processing
Journal of Mathematical Imaging and Vision
Stack filter design: a structural approach
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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For binary window-based filters, the optimal increasing filter is often derived from the optimal unconstrained (nonincreasing) filter by iteratively switching the filter values at pixels from 0 to 1 or from 1 to 0 so as to make the resulting filter be the optimal increasing filter. This paper gives a corresponding switching algorithm for gray-scale nonlinear filters, and it does so in the context of finite lattices, which makes the algorithm applicable to computational morphology on lattices. The algorithm is minimal in the sense that it involves a minimal search if one wishes to be certain to obtain the optimal increasing filter when beginning with the optimal unconstrained filter.