Computer Vision & Autonomous Systems
Computer-based analysis of images to extract information and classify their contents is becoming increasingly important in all walks of life. For example, by combining the science of 'photogrammetry' (measurement using cameras) with digital camera technology it becomes possible to capture 3D models of people, animals and objects that are metrically accurate and photo-realistic in appearance. Furthermore, it is possible to analyse and animate these models by computer for applications such as virtual actors or sports science.
The Computer Vision and Autonomous Systems group, CVAS, in the School of Computing Science, investigates fundamental issues of how to analyse images and also how apply this knowledge within practical applications. Our projects cover all aspects of human body modelling in 3D, including animation and surface skin modelling. This approach opens a wide array of application areas such as; creative media, engineering, medicine, textiles & clothing, military & security, internet & communications, forensic and fine art. A key objective of the work of the group is to combine 3D measurement and modelling techniques with image understanding approaches to construct cognitive robot vision systems that actively search their operating environments using passive digital cameras.
CVG research topics
- CLOPEMA robotic cloth folding project.
- Design of programming languages capable of harnessing parallelism available in vector instruction sets and GPUs and multi-core processors.
- Vector Pascal Compilers for AVX and MIC style machines
- Lino for the SCC
- Oilfield visualisation
- Landmine visualisation using Ground Penetrating Radar
- Physical foundations of computability
- Active gaze control and cognitive vision system within a binocular robot sensor head.
- Regularisation of disparity maps based on intensity edge guided anisotropic diffusion.
- Analysis of face range map scans by means of dense vector fields.
- Combined 2D/3D intensity image and range scan analysis of free-form objects exhibiting biological variation.
- The Face3D consortium carrying out research into the analysis of three-dimensional facial dysmorphology.
- Reinforcement learning for clothing manipulation
- Medical and veterinary analysis of 3D surface anatomy to assess change following surgical intervention and surgical outcome prediction.
- Object recognition from 2D & 3D information extracted from static images and moving image sequences.
- Biologically motivated computer vision, including computational models of the mammalian retina and the early visual pathway for efficient and robust image analysis and interpretation.
- Active binocular robot vision systems, able to operate in unstructured and cluttered real-world environments searching and locating visual cues and objects required in autonomous applications such as unmanned vehicle navigation, flexible manufacture, telemedicine and suspicious object inspection.
- 2D and 3D image compression.
- 3D electron tomography.
- Parallelisation of optical flow.
- Hiearchical visual featue extraction.
Honorary Research Fellow: Dr John W Patterson.
Research Students: Mr Aamir Khan, Mr Finlay McCourt, Xiaomeng Wang, Lai Meng Tang, Long chen,