Image Understanding
At Microsoft Research in Cambridge we are developing new machine vision algorithms for automatic recognition and segmentation of many different object categories. We are interested in both the supervised and unsupervised scenarios.
Demos
(click to view demo videos)
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L O C U S
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Research data
(click to download labelled image databases for supervised learning)
Note that the data provided here may be used freely for research purposes but it cannot be used for commercial purposes.
- Database of thousands of weakly labelled, high-res images. Please, click here to download the database.
- Pixel-wise labelled image database v1 (240 images, 9 object classes). Please, click here to download the database. This database was used in paper 1 below and in the above demo video. Please click here to read the full license.
- Pixel-wise labelled image database v2(591 images, 23 object classes). Please, click here to download the database. Please click here to read the full license.
Publications
2011
- Florian Schroff, Antonio Criminisi, and Andrew Zissermann, Harvesting Image Databases from the Web, in IEEE PAMI, April 2011
2009
- Antonio Criminisi, Jamie Shotton, Stefano Bucciarelli, and Khan Siddiqui, Automatic Semantic Parsing of CT Scans via Multiple Randomized Decision Trees, in Radiological Society of North America (RSNA), December 2009
- Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman, The Pascal Visual Object Classes (VOC) Challenge, in International Journal of Computer Vision, vol. 88, no. 2, pp. 303-308, Springer Verlag, 9 September 2009
- Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI special issue), IEEE, 2009
- Minh Hoai Nguyen, Lorenzo Torresani, Fernando de la Torre, and Carsten Rother, Weakly supervised discriminative localization and classification: a joint learning process, in ICCV, 2009
- Antonio Criminisi, Jamie Shotton, and Stefano Bucciarelli, Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes, in MICCAI workshop on Probabilistic Models for Medical Image Analysis (MICCAI-PMMIA), 2009
2008
- Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD., IEEE Computer Society, 2008
- Florian Schroff, Antonio Criminisi, and Andrew Zisserman, Object Class Segmentation using Random Forests, in Proc. British Machine Vision Conference (BMVC), 2008
2007
- Pei Yin, Antonio Criminisi, Irfan Essa, and John Winn, Tree-based Classifiers for Bilayer Video Segmentation, in Proc. Conf. Computer Vision and Pattern Recognition, 2007
- Thomas Deselaers, Antonio Criminisi, John Winn, and Ankur Agarwal, Incorporating On-demand Stereo for Real Time Recognition, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2007
- G. Florian Schroff, Andrew Zisserman, and Antonio Criminisi, Harvesting Images Databases from the Web, in Proc. IEEE Intl. Conference on Computer Vision (ICCV), 2007
- Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John Winn, Andrew Blake, and Andrew Fitzgibbon, C-Slate: Exploring Remote Collaboration on Horizontal Multi-touch Surfaces. , in Proc. IEEE Tabletop, 2007
2006
- Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in NIPS, 2006
- Silvio Savarese, Antonio Criminisi, and John Winn, Discriminative Object Class Models of Appearance and Shape by Correlatons, in Proc. IEEE Computer Vision and Pattern Recognition (CVPR)., January 2006
- Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi, TextonBoost: Joint Appearance, Shape and Context Modeling for Mulit-Class Object Recognition and Segmentation, in European Conference on Computer Vision (ECCV), January 2006
- John Winn and Antonio Criminisi, Object Class Recognition at a Glance, in Proc. Conf. Computer Vision and Pattern Recognition (CVPR) -- Video Track, 2006
- G. Florian Schroff, Antonio Criminisi, and Andrew Zisserman, Single-histogram Class Models for Image Segmentation, in Proc. Indian Conference on Computer Vision, 2006
2005
- John Winn, Antonio Criminisi, and Thomas Minka, Object Categorization by Learned Universal Visual Dictionary, in Proc. IEEE Intl. Conf. on Computer Vision (ICCV)., January 2005