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


    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.


    Anfisa’s PhD viva was successful, and will be making minor corrections to her thesis.
    Muhammad Asad’s PhD viva was successful, and will be making minor corrections to his thesis.
    Anfisa Lazareva is now an employee of Mirada Medical Ltd.
    Three papers accepted to SPIE Medical Imaging 2017, including Nathan's paper, "Pairwise Mixture Model for Unmixing Partial Volume Effect in Multi-voxel MR Spectroscopy of Brain Tumour Patients".