Grant: Discovery Grants Program - Individual

Automatic computer/robot vision for manufacturing, health care, security, and multi-media is still largely ""work in progress"" and relatively few methods produce consistently reliable results on real data. Sheer size of high-resolution volumetric images in stereo-vision, motion analysis, computer-assisted diagnosis (e.g. MRI, CT), and other applications demands very high level of efficiency from computer vision algorithms. Despite significant progress in the last 10-20 years, the vision community is still widely researching new theories and mathematical concepts that could lead to computationally feasible methods for image segmentation, shape representation, model fitting, stereo, and many other low-level problems forming the base for all computer vision systems. My research concentrates on practical computationally-efficient and mathematically solid models for low-level vision. This topic offers an exciting ground for creative interdisciplinary research linking optimization, statistical physics, information theory, learning, applied differential geometry, and other mathematical sciences. Most low-level vision problems can be formulated as optimization problems using either information-based methodology (e.g. minimum description length principle), or concepts from statistical physics (e.g. posterior energy), or differential geometry (minimum surface). The focus of my proposed research are mathematically solid models in low-level vision and the corresponding fast optimization methods computing either their global minimum or some guaranteed-quality approximation. Such optimization problems are a challenge for the state of the art in discrete combinatorial algorithms and continuous variational techniques. Firstly, it was shown that many problems in vision are intrinsically difficult (NP-hard), thus effective approximations must be explored. Secondly, computational efficiency and scalability of the proposed optimization algorithms is crucial when a solution is sought on huge 3D or 4D image volumes common in vision and medical imaging.