DGMM 2016 - Workshop on Discrete Geometry and Mathematical Morphology for Computer Vision

Discrete geometry plays a fundamental role in research fields such as image analysis, computer vision, computer graphics, pattern recognition, and shape modelling. This is because all data in the computer are unavoidably discrete. The foundation of discrete geometry comes from the necessity of the treatment of digitized models or images of objects in the 2D or 3D Euclidean space. Mathematical morphology, on the other hand, is a theory and technique for analyzing and processing geometrical structures based on set theory, lattice theory, and topology. Mathematical morphology is most commonly applied to digital images, but it can be employed as well on graphs, surface meshes, solids, and many other spatial structures.
The community of discrete geometry and that of mathematical morphology have closely communicated and mutually exchanged latest research results and ideas to stimulate and widen the communities. However, the computer vision community has less communicated with both the communities in spite that they all are working for digital images. Sharing recent findings in each community with the other communities contributes to advancing the cutting edge of researches for image analysis. There are increased demands to exchange latest results and ideas to foster these three communities together, and it is quite timely to bring them together.
Scope
Based on the recent development and discovery in 2D and 3D image analysis, the goal of this workshop is to light up and share common digital and discrete methodology in various fields and to open a new direction of computer vision, discrete geometry and mathematical morphology. Successful researchers are expected to submit their latest results and new ideas in discrete and digital geometric methods in image analysis and its related areas.
Main topics are, but are not limited to:
Theory:
Geometric descriptors
Object digitization
Geometric transformation
Geometric motion analysis
Graph-based method
Markov random field
Discrete and combinatorial optimization
Connected operators
Hierarchical analysis
Computational topology
Applications:
Low-level vision, image processing
Denoising and filtering
Segmentation and grouping
Object detection
Model fitting
Point cloud processing
Image registration
Surface generation
Motion segmentation
Video segmentation
Motion tracking
Biomedical analysis
Human action recognition
Scene labelling and understanding
Medical image processing
Discrete calculus