Optimal mean-square N-observation digital morphological filters: i. optimal binary filters
CVGIP: Image Understanding
Optimal stack filters under rank selection and structural constraints
Signal Processing
Secondarily constrained Boolean filters
Signal Processing
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Binary Filter Estimation for Large Windows
SIBGRAPHI '98 Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision
Pattern Recognition Theory in Nonlinear Signal Processing
Journal of Mathematical Imaging and Vision
Multiresolution Design of Aperture Operators
Journal of Mathematical Imaging and Vision
Design of optimal binary filters under joint multiresolution-envelope constraint
Pattern Recognition Letters - Special issue: Sibgrapi 2001
Optimal Filters with Multiresolution Apertures
Journal of Mathematical Imaging and Vision
Nonlinear Filter Design Using Envelopes
Journal of Mathematical Imaging and Vision
Generating segmented meshes from textured color images
Journal of Visual Communication and Image Representation
An Information Theory framework for two-stage binary image operator design
Pattern Recognition Letters
Automatic window design for gray-scale image processing based on entropy minimization
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Artificial neural networks applied to statistical design of window operators
Pattern Recognition Letters
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The performance of a designed digital filter is measured by the sum of the errors of the optimal filter and the estimation error. Viewing an image at a high resolution results in optimal filters having smaller errors than at lower resolutions; however, higher resolutions bring increased estimation error. Hence, choosing an appropriate resolution for filter design is important. The present paper provides expressions for both the error of the optimal filter and the design error for estimating optimal filters in a pyramidal multiresolution framework. The analysis is facilitated by a general characterization of suitable sequences of resolution-constraint mappings. The error expressions are generated from resolution to resolution in a telescoping manner. To take advantage of data at all resolutions, one can use a hybrid multiresolution design to arrive at a multiresolution filter. A sequence of filters is designed using data at increasing resolutions, each filter serves as a prior filter for the next, and the last filter is taken as the designed filter. The value of the multiresolution filter at a given observation is based on the highest resolution at which conditioning by the observation is considered significant.