Introduction to mathematical morphology
Computer Vision, Graphics, and Image Processing
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological methods in image and signal processing
Morphological methods in image and signal processing
A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Morphological Pattern Restoration from Noisy Binary Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal morphological representation and restoration of binary images: theory and applications
Optimal morphological representation and restoration of binary images: theory and applications
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
Two-Dimensional Digital Signal Processing II: Transforms and Median Filters
Two-Dimensional Digital Signal Processing II: Transforms and Median Filters
A fast thresholded linear convolution representation of morphological operations
IEEE Transactions on Image Processing
Morphological representation of order-statistics filters
IEEE Transactions on Image Processing
Vehicle number plate recognition using mathematical morphology and neural networks
WSEAS Transactions on Computers
Morphological image enhancement procedure design by using genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Efficient non-linear filter for impulse noise removal in document images
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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In this paper, we derive the optimal structuring elements of morphological filters in image restoration. The expected pattern transformation of random sets is presented. An estimation theory framework for random sets is subsequently proposed. This framework is based on the least mean difference (LMD) estimator. The LMD estimator is defined to minimize the cardinality of the expected pattern transformation of the set-difference of the parameter and the estimate. Several important results for the determination of the LMD estimator are derived. The LMD structuring elements of morphological filters in image restoration are finally derived.