Modeling Imprecise and Bipolar Algebraic and Topological Relations using Morphological Dilations

Mathematical morphology is a well established methodology for the analysis of geometrical structures. Its theoretical roots are based on set theory, topology, stochastic geometry, lattice theory, nonlinear partial differential equations, etc. Mathematical morphology is applied to process digital images and other forms of spatial structures as graphs, surface meshes, data clouds, etc.

Mathematical Morphology - Theory and Applications (MMTA) is an Open Access electronic journal publishing either purely theoretical advances, or new ways of applying mathematical morphology to real-world problems.
MMTA serves also as a forum open to other related mathematical image processing approaches as discrete geometry, topological imaging and scale-space models.

Accepted papers are published on ongoing basis. The journal publishes:

• Research Articles
• Survey Papers
• Pedagogical Papers
• Short Communications
• Letters to the Editor, Amendments, and Commentaries.

Mathematical Morphology - Theory and Applications is devoted to the publication on the following topics:

• Algebraic Theory: Morphology on complete lattices and semilattices, Representation of morphological operators, Fuzzy morphology, Connected operators, Morphology on graphs, Morphology on Surface Meshes and Riemannian manifolds.
• Nonlinear Scale Space Theory: Morphological decompositions, Morphological PDEs, Level set methods, Morphological wavelets, Morphological regularization.
• Discrete Geometry and Combinatorial Topology: Grids, Discrete objects, Discrete model properties, Digitization schemes, Metrics, etc.
• Random sets Theory and Geometrical Probability: Boolean model for sets and functions, Stochastic simulation of random media, etc.
• (max,+) Mathematics and Idempotent Analysis for Image and Signal Processing.
• Image Filtering: Colour and multi-channel morphology, Morphology on tensor fields, Geodesic transformations, Adaptive morphology, Attribute filtering.
• Image Segmentation: Watershed segmentation, Hierarchical segmentation, Colour and multi-channel image segmentation, Texture segmentation, Clustering of spatial data
• Computational Mathematical Morphology: Algorithms, Architectures, Data structures and programming paradigms for efficient implementation of morphological operators.
• Applications: Geoscience and remote sensing, Biomedical imaging, Materials science, Data analysis, Document processing, Content-based information retrieval, Video surveillance, Industrial control, Visualisation, etc.