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|
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.
Gerard Pons-Moll, Jonathan Taylor, Jamie Shotton, Aaron Hertzmann, and Andrew Fitzgibbon, Metric Regression Forests for Correspondence Estimation, in IJCV, Springer, August 2015.
Wangjiang Zhu, Baoyuan Wang, and Steve Lin, Adaptive Pooling over Multiple Trajectory Attributes for Action Recognition, in AVSS 2015, 12th IEEE International Conference On Advanced Video and Signal-based Survillance, August 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
- 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
- Alternating Minimization for Non-convex Optimization Problems