General
Mathematical Morphology can be defined as a discipline for the analysis of spatial structures. It is called morphology because the analysis concentrates on the shape, form, and neighbourhood relations of these structures. It is mathematical because it relies on set theory (with emphasis on lattice theory), geometry, and topology. Mathematical Morphology offers to practitioners a wide range of effective methods and efficient algorithms for analysing digital images and other forms of spatial data.

The International Symposium on Mathematical Morphology (ISMM) has established itself as the main scientific event in the field. This edition of ISMM will be the tenth in the series initiated in 1993 in Barcelona and will be held for the first time in Italy. The goal of the conference is to bring together researchers, students, and practitioners of Mathematical Morphology to present and discuss new advances on topics ranging from new theoretical developments to novel applications solving complex image analysis problems. For this anniversary conference, special attention will be given to developments and applications related to the analysis and processing of geospatial data.

Main topics of interest (non-exclusive list):
Lattice theory: Morphology on complete and semi-lattice - Representation of morphological operators - Fuzzy morphology;
Geometry and Topology: Discrete topology and geometry - Metrics and distance transforms - Random sets and geometrical probability - Shape analysis;
Signal Processing: Level set methods - Morphological PDEs - Morphological wavelets - Multiresolution and scale spaces;
Image Processing: Color and multi-channel morphology - Geodesic transformations - Adaptive morphology - Attribute filtering - Image simplification - Morphological pattern recognition;
Connectivity: Connected operators - Connectivity theory - Morphology on graphs;
Segmentation: Color image segmentation - Hierarchical segmentation - Texture segmentation - Watershed segmentation - Clustering of spatial data;
Algorithms and Architectures: Efficient implementations - Data structures for morphology - Performance evaluation of algorithms - GPU implementations;
Applications: Geoscience and remote sensing - Biomedical imaging - Data analysis - Document processing - Materials science - Video-surveillance - Quality control - Visualisation;
Morphological methods for the analysis of geospatial data: Automatic information retrieval from satellite image data - Processing of hyperspectral, VHR, and SAR imagery - Analysis of digital elevation;