Conferences  >  Mathematics  >  Pattern Recognition and Image Processing, Machine Vision  >  Canada

The Computer Vision Foundation (CVF) | IEEE Computer Society

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

Banff International Research Station (BIRS) / UBCO

Recent remarkable advances in learning-based methods are revolutionizing the field of image analysis resulting in a paradigm shift towards data-driven approaches. These methods have already shown tremendous success in a wide range of applications from computer vision such as object detection and object category classifications. To perform efficiently, such methods typically require a large number of training sample and this requirement is highly impractical or impossible in applications from medical imaging where it is very laborious and expensive to generate labeled data. Another drawback of learning-based method is that they do not come with provable performance guarantees. One remedy to such limitations is to combine data-driven methods with prior knowledge through mathematical and/or physical models in such a way to optimize processing, reconstruction and analysis of medical imaging data without the need of extensive training. This workshop will bring together researchers with a different background ranging from optimization, inverse problem, numerical and harmonic analysis and machine learning to advance state-of-the-art methods combining data- and model-driven approaches for medical imaging. To create a more engaging and productive environment, participants are selected from a diverse pool of researchers from multiple institutions, geographic regions, ethnic background and at different career levels.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

We will use nonlinearity naturally arising in many equations in mathematical physics to facilitate the imaging image reconstruction. This breaks with the traditional doctrine that the presence of nonlinearity is an obstacle rather than a tool for understanding differential equations.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

The advances in imaging techniques have enabled the access to 3D shapes present in a variety of biological structures: organs, cells, organelles, and proteins. Since biological shapes are related to physiological functions, biomedical analyses are poised to incorporate more morphological data. For example, at the macroscopic scale, characterizing brain morphologies allows clinicians to quantify the progression of Alzheimer's disease. At the microscopic scale, the characterization of protein morphologies allows biologists to understand how these biomolecules react to chemical variations of their environment and helps detect promising pharmacological targets for the treatment of conditions ranging from neurological disorders to several cancers. Therefore, different biological scales ask a common statistical question: how can we build mathematical and statistical descriptions of biological morphologies and their variations? This workshop invites participants from different application fields to exchange mathematical and statistical methods for the study of biological shapes.

Biophysics, Medical Physics,    Systems Biology and Computational Biology