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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

Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time Hand Tracking, CHI, April 2015

Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore, Quantum Nearest-neighbor Algorithms for Machine Learning, in Quantum Information and Computation, vol. 15, no. 3&4, pp. 0318-0358, Rinton Press, March 2015

Kevin Schelten, Sebastian Nowozin, Jeremy Jancsary, Carsten Rother, and Stefan Roth, Interleaved Regression Tree Field Cascades for Blind Image Deconvolution, IEEE – Institute of Electrical and Electronics Engineers, 6 January 2015

Amr Sharaf, Marwan Torki, Mohamed E. Hussein, and Motaz El-Saban, Real-time Multi-scale Action Detection From 3D Skeleton Data, IEEE – Institute of Electrical and Electronics Engineers, January 2015

More publications...