Mathematical Morphology and Its Application to Signal and Image Processing: 9th International Symposium on Mathematical Morphology, ISMM 2009 Groningen, The Netherlands, August 24-27, 2009 Proceedings / Edition 1by Michael H. F. Wilkinson
Pub. Date: 09/01/2009
Publisher: Springer Berlin Heidelberg
The 9th ISMM conference covered a very diverse collection of papers, bound together by the central themes of mathematical morphology, namely, the tre- ment of images in terms of set and lattice theory. Notwithstanding this central theme, this ISMM showed increasing interaction with otherelds of image and signal processing, and several hybrid methods were
The 9th ISMM conference covered a very diverse collection of papers, bound together by the central themes of mathematical morphology, namely, the tre- ment of images in terms of set and lattice theory. Notwithstanding this central theme, this ISMM showed increasing interaction with otherelds of image and signal processing, and several hybrid methods were presented, which combine the strengths of traditional morphological methods with those of, for example, linearltering.This trendis particularlystrong in the emerging field of adaptive morphologicalltering, where the local shape of structuring elements is det- mined by non-morphological techniques. This builds on previous developments of PDE-based methods in morphology and amoebas. In segmentation we see similar advancements, in the development of morphological active contours. Even within morphology itself, diversification is great, and many new areas of research are being opened up. In particular, morphology of graph-based and complex-based image representations are being explored. Likewise, in the we- established area of connectedltering wend new theory and new algorithms, but also expansion into the direction of hyperconnectedlters. New advances in morphological machine learning, multi-valued and fuzzy morphology are also presented. Notwithstanding the often highly theoretical reputation of mathematical morphology, practitioners in thiseld have always had an eye for the practical.
- Springer Berlin Heidelberg
- Publication date:
- Lecture Notes in Computer Science / Image Processing, Computer Vision, Pattern Recognition, and Graphics Series, #5720
- Edition description:
- Product dimensions:
- 6.10(w) x 9.30(h) x 0.70(d)
Table of Contents
Invited Speaker.- Discrete Driver Assistance.- Theory.- The “False Colour” Problem.- Bipolar Fuzzy Mathematical Morphology for Spatial Reasoning.- An Axiomatic Approach to Hyperconnectivity.- Hyperconnectivity, Attribute-Space Connectivity and Path Openings: Theoretical Relationships.- Connectivity and Connected Filters.- Constrained Connectivity and Transition Regions.- Surface-Area-Based Attribute Filtering in 3D.- Levelings and Geodesic Reconstructions.- A Comparison of Spatial Pattern Spectra.- Adaptive Morphology.- Differential Equations for Morphological Amoebas.- Spatially-Variant Anisotropic Morphological Filters Driven by Gradient Fields.- 3-D Extraction of Fibres from Microtomographic Images of Fibre-Reinforced Composite Materials.- Spatially-Variant Morpho-Hessian Filter: Efficient Implementation and Application.- Graphs and Topology.- Some Morphological Operators in Graph Spaces.- Morphology on Graphs and Minimum Spanning Trees.- Segmentation.- Segmentation of Complex Images Based on Component-Trees: Methodological Tools.- Ultrametric Watersheds.- An Efficient Morphological Active Surface Model for Volumetric Image Segmentation.- Shape.- Ultimate Attribute Opening Segmentation with Shape Information.- Hierarchical Shape Decomposition via Level Sets.- Morphological Exploration of Shape Spaces.- Morphology of Multi-valued Images.- From Scalar-Valued Images to Hypercomplex Representations and Derived Total Orderings for Morphological Operators.- A Directional Rouy-Tourin Scheme for Adaptive Matrix-Valued Morphology.- Component-Trees and Multi-value Images: A Comparative Study.- Algorithms.- Fast Implementation of the Ultimate Opening.- Stack Filter Classifiers.- Milena: Write Generic Morphological Algorithms Once, Run on Many Kinds of Images.- An Efficient Algorithm for Computing Multi-scale Connectivity Measures.
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