The Computational Vision and Imaging Technologies (CVIT) research group is an interdisciplinary research group, providing a synthesis between the scientific disciplines associated with computational vision, image science, and image engineering and visual science. CVIT is a DCDI cross-college group, with members from the School of Computer Science and Engineering (where the group is based) and the Westminster School of Arts, having long-standing records in research activities relating to the above scientific fields.
CVIT activities include physical and computational measurement of images and imaging system characteristics, methods for image analysis, image reconstruction, content detection/identification/retrieval and interactive visual environments. Inter-relationships between physical measures, computational methods and models and image perception are major group interests.
Research areas, expertise and applications include:
- Digital image quality and metrics
- Imaging system performance
- Image psychophysics
- Visual modeling
- Image processing, analysis and interpretation
- Object detection/recognition
- Interactive visual environments
- Colour imaging and management
- Computational medicine
- Imaging applications (mobile, automotive medical, forensic, digitization & archiving)
In the last decade CVIT has had close collaborations and run joint research projects with the Royal Photographic Society, the Home Office’s Centre for Applied Science and Technology, the MoD’s Defence Science and Technology Laboratory (DSTL). Industry partners have included Nokia, Huawei, and SpectralEdge.
Our research laboratories are equipped with specialist software and apparatus that enable attributes of images and systems to be analysed and quantified, while state-of-the art psychophysical laboratories are dedicated to visual image evaluations.
At the Computational Vision and Imaging Technology (CVIT) we run a diverse and well-regarded research degree programme, reinforced by our Graduate School. Current PhD projects running at the CVIT include:
- Visual image quality modelling for engineering purposes
- Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis
- Modulation Transfer Function Determination from Pictorial Natural Scenes
- Deep Learning using Serious Games: an Application for Andragogy in Human Resource Development
If you have are interested in CVIT research please contact Sophie Triantaphillidou, the leader of CVIT.