A. Criminisi, J. Winn, C. Rother, N. Jojic, T. Minka, A. Blake, C. Bishop

 

Project description

  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 below to play demo videos)

    

 

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Epitomic Location Recognition

 

 

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Interactive Object Class Recognition (demo video related to paper 2 below) 

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Object Class Recognition at a Glance

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 LOCUS  (Learning Object Classes with Unsupervised Segmentation)

 

 Data              (labelled image databases for supervised learning approaches)
   Note that the data provided here may be used freely for research purposes but it cannot be used for commercial purposes. Please click here to read the full license.
 A) Database of thousands of weakly labelled, high-res images.  

Please, click here to download the database.
 B1) 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.
 B2) Pixel-wise labelled image database v2 (591 images, 23 object classes).

Please, click here to download the database.
 C) Pixel-wise labelled image database of textile materials.

Please, click here to download the database.

 

 Scientific publications             
  1. J. Shotton, J. Winn, C. Rother and A. Criminisi. TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation. In Proc. European Conference on Computer Vision (ECCV), Graz, Austria, 2006.
  2. J. Winn, A. Criminisi and T. Minka. Object Categorization by Learned Universal Visual Dictionary. In Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing, China, 2005.
  3. J. Winn and N. Jojic. LOCUS: Learning Object Classes with Unsupervised Segmentation. In Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing, China, 2005. 
  4. I. Ulusoy and C. M. Bishop. Generative Versus Discriminative Methods for Object Recognition. In Proc. IEEE Cont. on Computer Vision and pattern Recognition (CVPR), San Diego, CA, 2005.
  5. C. M. Bishop and I. Ulusoy. Object Recognition via Local Patch Labeling. In Proc. Workshop on Machine Learning, Sheffield, UK, 2005.

  6. J. Shotton, A. Blake and R. Cipolla. Contour-based learning for object recognition. In Proc. Int Conf. Computer Vision (ICCV), 1, 503–510, 2005.

  7. O. Williams, A. Blake, and R. Cipolla. The Variational Ising Classifier (VIC) algorithm for coherently contaminated data. Proc Neural Information Processing Systems, 17, 2004.

  8. K. Ni, A. Kannan, A. Criminisi and J. Winn. Epitomic Location Recognition. In Proc. Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, US, 2008.
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