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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, B. Corish, J. Dorn, P. Kontschieder, K. O'Hara, ASSESS MS Team, A. Criminisi, and A. Sellen, Assessing Multiple Sclerosis with Kinect: Designing Computer Vision Systems for Real-World Use, in Human-Computer Interaction, January 2016.

Simon Korman, Eyal Ofek, and Shay Avidan, Peeking Template Matching for Depth Extension, in The International Conference on Computer Vision 2015, IEEE – Institute of Electrical and Electronics Engineers, 13 December 2015.

P. Kontschieder, M. Fiterau, A. Criminisi, and S. Rota Bulo', Deep Neural Decision Forests, in Intl. Conf. on Computer Vision (ICCV), December 2015.

James Steven Supančič III, Grégory Rogez, Yi Yang, Jamie Shotton, and Deva Ramanan, Depth-based hand pose estimation: data, methods, and challenges, in Proc. ICCV, IEEE – Institute of Electrical and Electronics Engineers, December 2015.

Danhang Tang, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, and Jamie Shotton, Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose, in Proc. ICCV, IEEE – Institute of Electrical and Electronics Engineers, December 2015.

More publications...