The analysis of morphological filters with multiple structuring elements
Computer Vision, Graphics, and Image Processing
Neurocomputations in Relational Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive rank order based filters
Signal Processing
Detection of singular points in fingerprint images
Pattern Recognition
Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Morphological/rank neural networks and their adaptive optimal design for image processing
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
A general axiomatic theory of intrinsically fuzzy mathematical morphologies
IEEE Transactions on Fuzzy Systems
Adaptive basis matrix for the morphological function processing opening and closing
IEEE Transactions on Image Processing
MRL-filters: a general class of nonlinear systems and their optimal design for image processing
IEEE Transactions on Image Processing
The min-max function differentiation and training of fuzzy neural networks
IEEE Transactions on Neural Networks
An impulsive noise color image filter using learning-based color morphological operations
Digital Signal Processing
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In this paper we first introduce a neural network implementationfor fuzzy morphological operators, and by means of a trainingmethod and differentiable equivalent representations for theoperators we then derive efficient adaptation algorithms to optimizethe structuring elements. Thus we are able to design fuzzy morphologicalfilters for processing multi-level or binary images. The convergencebehavior of basic structuring elements and its significance forother structuring elements of different shape is discussed. Besidesthe filter design, the localized structuring elements obtainedfrom the training method give a structural characterization ofthe image which is useful in many applications. The performanceof the fuzzy morphological filters in removing impulse noisein multi-level and binary images is illustrated and comparedwith existing procedures.