Proceedings ArticleDOI
The use of first-order structuring-element libraries to design morphological filters
Robert P. Loce,Edward R. Dougherty +1 more
- Vol. 1, pp 256-259
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TLDR
The paper sets down a paradigm for library optimization and presents a methodology for first-order-library construction, which can greatly reduce design computation, while at the same time producing good filters.Abstract:
Statistically optimized morphological filters are preferable to those traditionally selected by humans. Nevertheless, full optimization has been shown to be computationally intractable. By applying first-order knowledge to select a predetermined structuring-element library upon which to apply optimization, one can greatly reduce design computation, while at the same time producing good filters. The paper sets down a paradigm for library optimization and presents a methodology for first-order-library construction. Experimental results depicted herein illustrate the goodness of the estimations. >read more
Citations
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Patent
Method of image enhancement using convolution kernels
TL;DR: In this paper, a set of structuring kernels is used to extract primitive shapes of pixel clusters, which are then used to determine whether each pixel, in turn, is to be treated as part of an image element such as an alphanumeric character.
Morphological filter mean-absolute-error representation theorems and their application to optimal morphological filter design
TL;DR: Central to the thesis is the MAE analysis for the various filter settings, where in each case, a theorem is derived that expresses overall filter MAE as a sum of MAE values of individual structuring-element filters and MAE of combina tions of unions of those elements.
Journal ArticleDOI
Constructing noise-reducing operators from training images
David B. Sher,Chris Y. Cheung +1 more
TL;DR: This paper discusses constructing non-linear noise reduction operators on binary images using a training set of noiseless images and extracts from the training set a probability distribution over local neighborhoods.
References
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Journal ArticleDOI
Optimal mean-square N -observation digital morphological filters: i. optimal binary filters
TL;DR: The present paper places binary morphological filtering into the framework of statistical estimation, the intent being to develop the theory of mean-square (MS) optimization.
Journal ArticleDOI
Facilitation of optimal binary morphological filter design via structuring element libraries and design constraints
TL;DR: This study analyzes two techniques for library construction: the expert approach involves prior sublibrary formation based on knowledge of important filter bases and the first-order approach employs single-erosion statistical information to limit the basis search to likely useful candidates.
Journal ArticleDOI
Optimal mean-square N -observation digital morphological filters: ii. optimal gray-scale filters
TL;DR: The result is a gray-scale morphological statistical estimation theory based on N observation random variables and a consequent theory of mean-square optimization that demonstrates that erosion optimization is equivalent to dilation optimization.
Journal ArticleDOI
Optimal morphological restoration: The morphological filter mean-absolute-error theorem
TL;DR: In this article, the mean absolute error of a morphological filter constructed from the Matheron expansion is analyzed in terms of mean absolute errors of single-erosion filters in both the binary and the gray-scale settings.
Proceedings ArticleDOI
Morphological filter mean-absolute-error theorem
TL;DR: In this paper, the authors provide expressions for the mean absolute error of general binary morphological filters formed from erosion bases in terms of mean absolute errors of single-erosion filters.