Vision, Graphics and Visualisation Group


Vision, Graphics and Visualisation Group


Vision, Graphics and Visualisation

There is a significant computer vision component within the IRG group, including: feature-based localisation of aerial platforms for planetary exploration; extraction of the 3D structure of complex objects; EPSRC-funded work on appearance-based methods to provide mobile robots with various capabilities including topological mapping and pose stabilisation. These areas have recently led to additional funding covering vision aspects in planetary science and topological data analysis.

Our vision research has gone beyond a supporting role for robotics, reflected in the Department’s recent strategy to further develop the vision research sub-group. Texture analysis and synthesis is used in image manipulation and medical image understanding: developments in the topological aspects of the domain are supported by EPSRC funding and are leading to improved understanding of human image evaluation. Registration has concentrated on computational geometry and other mathematical properties of images and volumes, recognition of articulated motion for evaluating human walking patterns, and medical shape analysis.

Research Topics

  • Visual Navigation and Mapping
  • Topological Data Analysis
  • 3D Registration and Reconstruction
  • Medical Image Understanding
  • Inverse Brain/Robotics Eng.

Recent Submissions

  • Lipsa, Dan R.; Laramee, Robert S.; Cox, Simon John; Davies, Ioan Tudur (2011-12-01)
    Research in the field of complex fluids such as polymer solutions, particulate suspensions and foams studies how the flow of fluids with different material parameters changes as a result of various constraints. Surface ...
  • Woodland, Alan; Labrosse, Frederic (Eurographics Association, 2005)
    Many computer vision and graphics related techniques rely upon illumination invariance of images to derive meaning from images of an object under varying lighting conditions. This is all the appearance-based methods. In ...
  • Yu, M.; Tiddeman, Bernard Paul (2010-02-01)
    In this paper, we describe a system for facial feature detection and tracking using a 3D extension of the Constrained Local Model (CLM) [Cris 06, Cris 08] algorithm. The use of a 3D shape model allows improved tracking ...
  • Chen, Jingying; Tiddeman, Bernard Paul (Springer Berlin / Heidelberg, 2008-07-01)
    An efficient and robust facial feature detection and tracking system is presented in this paper. The system is capable of locating a human face automatically. Six facial feature points (pupils, nostrils and mouth corners) ...
  • Hunter, D. W.; Tiddeman, Bernard Paul (INSTICC Press, 2009-02-05)
    The ability to synthesise the effects of ageing in human faces has numerous uses from aiding the search for missing people to improving recognition algorithms and aiding surgical planning. The principal contribution of ...

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