Imaging
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.
Projects
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3D feature extraction and matching
Feature extraction and matching is a traditional method for shape matching and analysis.
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Advanced Interfaces for Surgical Interventions
The development and use of medical virtual environments is one of the major research themes within RIVIC.
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Advanced Medical Imaging and Visualization Unit
The unit work with projects where imaging and visualization technologies can provide added value to medical applications.
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Automatic 3D Free Form Surface Modelling and Analysis in the Frequency Space from Range Images
Automatic 3D Free Form Surface Modelling with analysis in the Frequency Space from Range Images.
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Efficient 3D shape matching and retrieval
This project attempts to develop efficient techniques for the representation and matching of 3D shapes.
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Fly4PET: Fly Algorithm in PET Reconstruction for Radiotherapy Treatment Planning
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.
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Modelling the Change in 3D Head Dynamics and Appearance
The development of 3D (and also 2D) statistical models of facial dynamics with applications to medical/dental, computer graphics, animation and computer vision.
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Noise removal and image enhancement
The captured either 2D or 3D data are usually unavoidably corrupted by imaging noise.
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Point Based Modelling and Simulation - Sampling
A low-discrepancy, blue-noise point set represents a continuous geometric object well, minimising discretisation artefacts.
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Quantifying image quality in computer graphics
This project is indented to provide means of comparing computer graphics results in a possibly effective and accurate way.
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Saliency detection for 3D surface reconstruction, segmentation and simplification
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.