Philip Torr did his PhD (DPhil) at the Robotics Research Group of the University of Oxford under Professor David Murray of the Active Vision Group. He worked for another three years at Oxford as a research fellow, and is currently a Visiting Fellow in Engineering Science at the University of Oxford, working closely with Prof. Zisserman and the ...

Sam Hare, Amir Saffari, Philip H.S. Torr, Efficient Online Structured Output Learning for Keypoint-Based Object Tracking, In the Proceedings IEEE Conference of Computer Vision and Pattern Recognition (CVPR), 2012(poster).

Philip Torr did his PhD (DPhil) at the Robotics Research Group of the University of Oxford under Professor David Murray of the Active Vision Group. He worked for another three years at Oxford as a research fellow, and is still maintains close contact as visiting fellow there.

Welcome to the Torr Vision Group (TVG) at the University of Oxford, led by Professor Philip Torr. View our publications and research opportunities.

Qiang Wang *, Li Zhang *, Luca Bertinetto *, Weiming Hu, Philip H.S. Torr * means equal contribution In this paper we illustrate how to perform both realtime object tracking and semi-supervised video object segmentation with a single simple approach.

and Philip Torr Abstract We present an open-source, real-time implementation of the interactive SemanticPaint system for geometric reconstruction, object-class segmentation and learning of 3D scenes described in [Valentin15].

Philip Hilaire Sean Torr Department of Engineering Science, University of Oxford bernard@robots.ox.ac.uk, philip.torr@eng.ox.ac.uk Abstract. Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmenta-

ImageSpirit: Verbal Guided Image Parsing. Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin, Vibhav Vineet, Paul Sturgess, Nigel Crook, Niloy Mitra, Philip Torr, ACM Transactions on Graphics, 2014. PDF

I am pursuing my DPhil in Engineering Science, supervised by Prof. Philip Torr, at the TVG group, which is part of the Engineering Department at the University of Oxford. My main areas of interest are computer vision and machine learning, and more specifically, the visual analysis of people using deep learning algorithms.

Philip H. S. Torr philip.torr@eng.ox.ac.uk Andrea Vedaldi vedaldi@robots.ox.ac.uk Department of Engineering Science University of Oxford Oxford, UK Abstract Deep Matching (DM) is a popular high-quality method for quasi-dense image match-ing. Despite its name, however, the original DM formulation does not yield a deep neural