John M. Winn

Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John M. Winn, Karsten M. Borgwardt: ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinformatics 28(7): 1001-1008 (2012)

Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc J. Van Gool, Josef Sivic, Christopher K. I. Williams, David Hogg: In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. 34(11): 2081-2082 (2012)

S. M. Ali Eslami, Nicolas Heess, John M. Winn: The Shape Boltzmann Machine: A strong model of object shape. CVPR 2012: 406-413

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn: Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Computation 23(3): 593-650 (2011)

Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa: Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 33(1): 30-42 (2011)

Nicolas Heess, Nicolas Le Roux, John M. Winn: Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) 2011: 9-16

Albert Montillo, Jamie Shotton, John M. Winn, Juan Eugenio Iglesias, Dimitris N. Metaxas, Antonio Criminisi: Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. IPMI 2011: 184-196

Nicolas Heess, Nicolas Le Roux, John M. Winn: Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs. CoRR abs/1107.3823 (2011)

Mark Everingham, Luc J. Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2): 303-338 (2010)

Oliver Stegle, Leopold Parts, Richard Durbin, John M. Winn: A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies. PLoS Computational Biology 6(5) (2010)

Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi: TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. International Journal of Computer Vision 81(1): 2-23 (2009)

Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn: Epitomic Location Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2158-2167 (2009)

Iain E. Buchan, John M. Winn, Christopher M. Bishop: A unified modeling approach to data-intensive healthcare. The Fourth Paradigm 2009: 91-97

Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn: Epitomic location recognition. CVPR 2008

Tom Minka, John M. Winn: Gates. NIPS 2008: 1073-1080

Oliver Stegle, Anitha Kannan, Richard Durbin, John M. Winn: Accounting for Non-genetic Factors Improves the Power of eQTL Studies. RECOMB 2008: 411-422

Jean-François Lalonde, Derek Hoiem, Alexei A. Efros, Carsten Rother, John M. Winn, Antonio Criminisi: Photo clip art. ACM Trans. Graph. 26(3): 3 (2007)

Thomas Deselaers, Antonio Criminisi, John M. Winn, Ankur Agarwal: Incorporating On-demand Stereo for Real Time Recognition. CVPR 2007

Derek Hoiem, Carsten Rother, John M. Winn: 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation. CVPR 2007

Julia A. Lasserre, Anitha Kannan, John M. Winn: Hybrid learning of large jigsaws. CVPR 2007

Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa: Tree-based Classifiers for Bilayer Video Segmentation. CVPR 2007

Jim C. Huang, Anitha Kannan, John M. Winn: Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. ISMB/ECCB (Supplement of Bioinformatics) 2007: 212-221

Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John M. Winn, Andrew Blake, Andrew W. Fitzgibbon: C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces. Tabletop 2007: 3-10

John M. Winn, Jamie Shotton: The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects. CVPR (1) 2006: 37-44

Nebojsa Jojic, John M. Winn, C. Lawrence Zitnick: Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video. CVPR (1) 2006: 117-124

Silvio Savarese, John M. Winn, Antonio Criminisi: Discriminative Object Class Models of Appearance and Shape by Correlatons. CVPR (2) 2006: 2033-2040

Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi: TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. ECCV (1) 2006: 1-15

Ashish Kapoor, John M. Winn: Located Hidden Random Fields: Learning Discriminative Parts for Object Detection. ECCV (3) 2006: 302-315

Anitha Kannan, John M. Winn, Carsten Rother: Clustering appearance and shape by learning jigsaws. NIPS 2006: 657-664

John M. Winn, Christopher M. Bishop: Variational Message Passing. Journal of Machine Learning Research 6: 661-694 (2005)

John M. Winn, Nebojsa Jojic: LOCUS: Learning Object Classes with Unsupervised Segmentation. ICCV 2005: 756-763

John M. Winn, Antonio Criminisi, Thomas P. Minka: Object Categorization by Learned Universal Visual Dictionary. ICCV 2005: 1800-1807

John M. Winn, Andrew Blake: Generative Affine Localisation and Tracking. NIPS 2004

Christopher M. Bishop, David J. Spiegelhalter, John M. Winn: VIBES: A Variational Inference Engine for Bayesian Networks. NIPS 2002: 777-784

Christopher M. Bishop, John M. Winn: Non-linear Bayesian Image Modelling. ECCV (1) 2000: 3-17