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Computer Vision

Teaching computers to understand the visual world

 

We want to change the way you interact with visual data.   We want to make your photos magical, we want to deeply understand images and videos from cameras everywhere: in your phone, on your Xbox, in your fridge, on robots, in cars, anywhere.   We want you to be able to find your stuff, answer questions, make fantastic new images.  And we do that by inventing new algorithms and thinking of new mathematical models for how images come to be.

 

Image understanding

Understanding images

Image understanding with tens of layers, millions of classes, billions of images.

 

Human motion capture for Kinect

Understanding Humans

So much of computer vision is ultimately for humans, images of humans are an important special case

Image and video editing

Making images better

Pictures are an important part of our lives, and computer vision gives us the tools to enjoy better pictures.

Discrete optimization

Learning and Optimization

Computer vision often requires the solution of especially large or difficult problems in machine learning and nonlinear optimization, and we innovate in these domains.

 

Models for Video

One view of video is "all of the above, but faster".   We also try to explore new representations of video and new modes of interaction

 

Where are we?

Localization problems occur everywhere, from augmented reality to medical imaging to 3D modelling.

 

Recent vision publications

C. Morrison, K. Huckvale, A. Sakar, P. Kontschieder, J. Dorn, S. Steinheimer, C. P. Kamm, J. Burggraaff, M. D'Souza, F. Dahlke, L. Kappos, B. Uitdehaag, A. Criminisi, and A. Sellen, Visualizing ubiquitously sensed measures of motor ability in multiple sclerosis for clinical use, October 2015.

J. Burggraaff, J. Dorn, M. D'Souza, C. P. Kamm, P. Tewarie, P. Kontschieder, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, L. Kappos, and B. M. J. Uitdehaag, Video-based paired-comparison ranking: a validation tool for fine-grained measurements of motor dysfunction in multiple sclerosis, in Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), October 2015.

M. D'Souza, J. Burggraaff, P. Kontschieder, J. Dorn, C.P.Kamm, S. Seinheimer, P. Tewarie, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, B Uitdehaag, and L. Kappos, Prediction of expanded disability status scale subscores of motor dysfunction in multiple sclerosis using depth-sensing computer vision, in Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), October 2015.

H. Lombaert, A. Criminisi, and N. Ayache, Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation, in Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer, October 2015.

Gerard Pons-Moll, Jonathan Taylor, Jamie Shotton, Aaron Hertzmann, and Andrew Fitzgibbon, Metric Regression Forests for Correspondence Estimation, in IJCV, Springer, August 2015.

More publications...