Shape Analysis Group

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The shape of an object lies at the interface between vision and cognition, yet general purpose theories of shape have been notoriously difficult to formulate. Drawing on techniques from singularity theory, partial differential equations, geometric flows and graph theory, our group is broadly concerned with the problem of shape analysis in computational vision, visual perception, bio-medicine, neuroscience and robotics. A key theme is the development of "generic" models, to support a notion of similarity between qualitatively similar shapes. Distinct from current trends in computer vision in representation learning from data using black box methods, we are interested in learning representations which are informed by regularities of the physical world and the objects within it. A key focus has been on the use of mathematical tools from differential geometry and group theory to allow for appropriate levels of abstraction of visual form. I am particularly interested in biological shape analysis using applied mathematics and computer vision. Students interested primarily in neural network based approaches, deep learning, applied machine learning, applications of computer vision and black box methods should seek other colleagues with expertise in these areas.