6th International Ssvm Conference


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Hotel Koldingfjord

Fjordvej, 154, 6000 Kolding, Denmark Kaart

The 6th International Conference on Scale Space and Variational Methods in Computer Vision will take place on 4-8 June 2017 in Denmark at the Hotel Koldingfjord.

SSVM (Scale Space and Variational Methods in Computer Vision) is a biennial conference devoted to mathematical aspects of Computer Vision and Image Analysis in general. It focuses on multiscale representation of image contents and also on processing and analysis methods using Partial Differential Equations (PDEs), The Calculus of Variation, infinite and finite dimensional optimization problems. They are strongly overlapping topics, as, for instance, multiscale representation is often associated to special PDEs, and the calculus of variations is a large source of PDEs. The methods on which SSVM focuses are essential to extraction of relevant information in the very high dimensional data that images are. They find applications in a range of disciplines, for instance diagnostic imaging, micro-geological analysis, robot vision, etc.

Since its start, SSVM has been a forum where mathematicians, computer scientists and vision scientists have met and exchanged research experiences within the fields of computer vision and image analysis, and all working on the intersection of Computer Science and Applied Mathematics. Interest range goes from basic research such as understanding of image structure, its links to human perception, to complex tasks such as segmentation, reconstruction, etc.

Conference topics include, but are not limited to:

Image analysis
Scale-space methods
Level sets methods
PDEs in image processing
Inverse problems in imaging
Compressed sensing
Optimization methods in imaging
Convex and non convex modeling and optimization in imaging
Restoration and reconstruction
Multi-scale shape analysis
3D imaging modalities
3D vision
Wavelets and image decomposition
Stereo reconstruction
Optical flow
Motion estimation
Surface modeling
Implicit surfaces
Shape from X

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