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.
|Irides: Attaining Quality, Responsiveness and Mobility for Virtual Reality Head-mounted Displays|
|RoomAlive: Magical Experiences Enabled by Scalable, Adaptive Projector-Camera Units|
|RetroDepth: Visual-based 3-D sensing and interaction|
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.
- Deep Learning Technology Center
- Interactive 3D Technologies
- Natural Interaction Research
- Machine Learning and Perception
- SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips
- RoomAlive Toolkit
- Depth from Time-of-Flight
- From Captions to Visual Concepts and Back
- Presenter Camera
- Eye Gaze Keyboard
- Human activity detection in RGBD videos
- Fully Articulated Hand Tracking
- ATL Cairo GPSP - Projects Ideas
- Learning to be a depth camera for close-range human capture and interaction
- Sparse Reflections Analysis
- User-Specific Hand Modeling from Monocular Depth Sequences
- Real-Time RGB-D Camera Relocalization
- Real-time 3D Reconstruction at Scale using Voxel Hashing
- Kinectrack: Agile 6-DoF Tracking Using a Projected Dot Pattern
- RetroDepth: 3D Silhouette Sensing for High-Precision Input On and Above Physical Surfaces
- Microsoft 3-Handpose dataset
- Eye-Gaze Tracking for Improved Natural User Interaction
- ViiBoard: Vision-enhanced Immersive Interaction with Touch Board