Department of Computer Science

Computer Vision Group

The Computer Vision Group conducts research in computational methods to extract information from images. The group specialises in analysis of visual information captured from conventional cameras, depth sensors, and medical imaging modalities. The research of the group focusses on detection and recognition in images/video, image segmentation, registration, and shape analysis using geometric models derived from image collections. The work incorporates computing, multi-dimensional signal processing, machine learning, and computational geometry, with applications to healthcare, human-computer interaction, and computer games. Greg image

Research Activities

  • Hand Orientation, Pose, and Gesture Recognition using RGB Sensors
  • Cone Counting in Adaptive Optics Imaging of the Retina
  • Cervical Spine Image Analysis: Segmentation of Vertebral Bodies and Fracture Detection
  • Cardiovascular Soft Plaque Detection and Quantification using CT Images
  • Pattern Classification of Neurological Disorders based upon fMRI
  • Brain Tumour Characterisation using MR Spectroscopy
  • Level of Detail Modeling in 3D Animated Shapes
  • Visual Ontologies

Achievements

    Atif Riaz won the Three Minute Thesis competition held by City, University of London.
    Greg Slabaugh won the Research Student Supervision Award.
    The Computer Vision Group was awarded a Titan X Pascal GPU through NVidia’s GPU Grant Program.

News

    Paper "Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network" accepted to BMVC 2017.
    Anfisa Lazareva is now an employee of Mirada Medical Ltd.