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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

J. Margeta, A.Criminisi, D.C.Lee, and N.Ayache, Recognizing Cardiac Magnetic Resonance Acquisition Planes using Finetuned Convolutional Neural Networks, in To appear in Computer Methods in Biomechanics and Biomedical Engineering, December 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.

J. Valentin, V. Vineet, M.-M. Cheng, D. Kim, J. Shotton, P. Kohli, M. Niessner, A. Criminisi, S. Izadi, and P. Torr, SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips, in ACM Trans. on Graphics (TOG), ACM – Association for Computing Machinery, August 2015.

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

Sameh Khamis, Jonathan Taylor, Jamie Shotton, Cem Keskin, Shahram Izadi, and Andrew Fitzgibbon, Learning an Efficient Model of Hand Shape Variation from Depth Images, in CVPR, IEEE – Institute of Electrical and Electronics Engineers, June 2015.

More publications...