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The science and understanding of images is a fundamental aspect of visual computing. Within this area, RIVIC is the home to leading research in high dynamic range imaging.
Feature extraction and matching is a traditional method for shape matching and analysis.
The development and use of medical virtual environments is one of the major research themes within RIVIC.
The unit work with projects where imaging and visualization technologies can provide added value to medical applications.
Automatic 3D Free Form Surface Modelling with analysis in the Frequency Space from Range Images.
This project attempts to develop efficient techniques for the representation and matching of 3D shapes.
This project is focused on developing new software technologies for lung cancer treatment and it is based on accurate physical models implemented using high performance computing.
The development of 3D (and also 2D) statistical models of facial dynamics with applications to medical/dental, computer graphics, animation and computer vision.
The captured either 2D or 3D data are usually unavoidably corrupted by imaging noise.
A low-discrepancy, blue-noise point set represents a continuous geometric object well, minimising discretisation artefacts.
This project is indented to provide means of comparing computer graphics results in a possibly effective and accurate way.
While the laser scanning systems usually have limited field of view, the captured data from a single viewpoint can only cover a part of the area of interest.