Introduction to mathematical morphology
Computer Vision, Graphics, and Image Processing
Computer Vision, Graphics, and Image Processing
Principles and practice of information theory
Principles and practice of information theory
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Discrete-time signal processing
Discrete-time signal processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Some Sequential Algorithms for a Generalized Distance Transformation Based on Minkowski Operations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Binary Morphological Bandpass Filters Induced byGranulometric Spectral Representation
Journal of Mathematical Imaging and Vision
Morphologically Constrained GRFs: Applications to Texture Synthesis and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An edge preserving noise smoothing technique using multiscale morphology
Signal Processing
Optimal Structuring Elements for the Morphological Pattern Restoration of Binary Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of optimal binary filters under joint multiresolution-envelope constraint
Pattern Recognition Letters - Special issue: Sibgrapi 2001
Optimal morphological restoration: The morphological filter mean-absolute-error theorem
Journal of Visual Communication and Image Representation
Iterative segmentation algorithms using morphological operations
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
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A theoretical analysis of morphological filters for the optimal restoration of noisy binary images is presented. The problem is formulated in a general form, and an optimal solution is obtained by using fundamental tools from mathematical morphology and decision theory. Consideration is given to the set-difference distance function as a measure of comparison between images. This function is used to introduce the mean-difference function as a quantitative measure of the degree of geometrical and topological distortion introduced by morphological filtering. It is proved that the class of alternating sequential filters is a set of parametric, smoothing morphological filters that best preserve the crucial structure of input images in the least-mean-difference sense.