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Research @ISPGroup

The ISPGroup is active in the following main research topics:

Sensing, Imaging and Rendering: What is the most efficient way to sense a signal or to image reality? How can the sensing stage already "compute" information in a way that "just the information we need" is captured? This is the general research topic that we address here. This covers fields like, inverse problem solving, compressed sensing and compressive imaging, data restoration techniques, hyperspectral and light field imaging, or virtual view point rendering.

Representation and Communication: How can we efficiently represent an image signal, or general data in as few parameter as possible? Which kind of (greedy) algorithm can deduce such parameters? How can this "sparse" representation be transmitted in a compact bitstream, potentially under latency and bitbudget constraints? How can we adapt the forwarded content to semantic user requirements? How can we build personalized summaries of edited video feeds automatically?

Analysis and Interpretation: Video are segmented into semantically meaningful objects, based on texture and motion analysis. Images descriptors are extracted to characterize visual contents. Targets of interest are detected, recognized, and tracked to understand behaviors in natural scenes. Application domains include autonomous production of visual reports (e.g. for team sport events), but also intelligent vision in surveillance, or cells images analysis in biology.

Medical Image Processing: Our group develops image & signal processing tools and models for the use in various medical contexts, including radiotherapy, proton therapy, brachytherapy, surgery, EEG analysis, kinematic, diffusion tensor imaging, etc.

Focus on a randomly picked research topic: (Click here to see them all)

Imaging the Brain Microstructure: Diffusion tensor imaging, the most common model of the diffusion signal, is unable to represent the signal arising from water molecules diffusing in different compartments such as multiple fascicles and the extra-axonal space. Multi-fascicle models overcome this limitation by providing a parametric representation for each compartment, allowing the development of new brain network modeling.


Last updated October 23, 2017, at 04:51 PM