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Threshold Decomposition of Gray-Scale Morphology into Binary Morphology
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Analysis of Thinning Algorithms Using Mathematical Morphology
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Threshold Superposition in Morphological Image Analysis Systems
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Discrete Black and White Object Recognition via Morphological Functions
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Morphological Shape Decomposition
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Decomposition of Convex Polygonal Morphological Structuring Elements into Neighborhood Subsets
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Optimal Morphological Pattern Restoration from Noisy Binary Images
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Theoretical Aspects of Gray-Level Morphology
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Cluster Analysis by Binary Morphology
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Decomposition of Arbitrarily Shaped Morphological Structuring Elements
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Acoustic imaging: the reconstruction of underwater objects
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Decomposition of binary morphological structuring elements based on genetic algorithms
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Improved segmentation of semiconductor defects using area sieves
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Geometric modeling based on triangle meshes
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Developing digital cartography in rural planning applications
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Iterative symmetry detection: Shrinking vs. decimating patterns
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For the purposes of object or defect identification required in industrial vision applications, the operations of mathematical morphology are more useful than the convolution operations employed in signal processing because the morphological operators relate directly to shape. The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations. Examples are given for each morphological concept and explanations are given for many of their interrelationships.