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Computer Vision

Teaching computers to understand the visual world


The goal of computer vision is to make computers efficiently perceive, process, and understand visual data such as images and videos. The ultimate goal is for computers to emulate the striking perceptual capability of human eyes and brains-or even to surpass and assist the human in certain ways.

Within Microsoft Research, our computer-vision research include investigations into:

  • Imaging and Photogrammetry, including high-resolution cameras, radiometric calibration, photometric stereo, 3-D imaging and video, 3-D scene reconstruction from images and video, and image and video enhancement.
  • Pattern Recognition and Statistical Learning, including data clustering and classification, manifold learning, and high-dimensional geometry and statistics.
  • Object Detection and Recognition, including face detection, alignment, and tagging; video-based face recognition; and sparsity-based robust face recognition. We also investigate general object-class recognition and advanced medical-image analysis.
  • Image and Video Editing and Enhancement, including denoising and deblurring, novel representations for images and video, techniques for content-aware edits such as in-painting, and object removal. 
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

Kenton O'Hara, Gerardo Gonzalez, Abigail Sellen, Graeme Penney, Varnavas, Helena Mentis, Antonio Criminisi, Robert Corish, Mark Rouncefield, Neville Dastur, and Tom Carrell, Touchless Interaction in Surgery, in Communications of the ACM, December 2014

Anitha Kannan and Simon Baker, Identifying Presentation Styles in Online Educational Videos, no. MSR-TR-2014-141, 6 November 2014

Yuwang Wang, Baoyuan Wang, Qionghai Dai, Yizhou Yu, and Zhuowen Tu, Action-Gons: Action Recognition with A Discriminative Dictionary of Structured Elements of Varying Granularity, ACCV, November 2014

More publications ...