Objective
In NoMADS we focus on data processing and analysis techniques which can feature potentially very complex, nonlocal, relationships within the data. In this context, methodologies such as spectral clustering, graph partitioning, and convolutional
neural networks have gained increasing attention in computer science and engineering within the last years, mainly from a combinatorial point of view. However, the use of nonlocal methods is often still restricted to academic pet projects. There is a large gap between the academic theories for nonlocal methods and their practical application to real-world problems. The reason these methods work so well in practice is far from fully understood.
Our aim is to bring together a strong international group of researchers from mathematics (applied and computational analysis, statistics, and optimisation), computer vision, biomedical imaging, and remote sensing, to fill the current gaps between theory and applications of nonlocal methods. We will study discrete and continuous limits of nonlocal models by means of mathematical analysis and optimisation techniques, resulting in investigations on scale-independent
properties of such methods, such as imposed smoothness of these models and their stability to noisy input data, as well as the development of resolution-independent, efficient and reliable computational techniques which scale well
with the size of the input data. As an overarching applied theme we focus in particular on image data arising in biology and medicine, which offers a rich playground for structured data processing and has direct impact on society, as well as discrete point clouds, which represent an ambitious target for unstructured data processing. Our long-term vision is to discover fundamental mathematical principles for the characterisation of nonlocal operators, the development of new robust and efficient algorithms, and the implementation of those in high quality software products for real-world application.
Fields of science
- natural sciencescomputer and information sciencessoftware
- natural sciencesmathematicspure mathematicsmathematical analysis
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Keywords
Programme(s)
Coordinator
91054 Erlangen
Germany
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Participants (24)
08002 Barcelona
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33000 Bordeaux
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14032 Caen Cedex 5
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14050 Caen
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CB2 1TN Cambridge
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16126 Genova
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32000 Haifa
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1049 001 Lisboa
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20133 Milano
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NG7 2RD Nottingham
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91128 Palaiseau Cedex
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7522 NB Enschede
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69622 Villeurbanne Cedex
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CB2 0AA Cambridge
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Participation ended
3190500 Haifa
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16124 Genova
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CB4 0HH Cambridge Cambs
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48341 Altenberge
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
14000 Caen
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
2333 AA Leiden
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
48149 MUENSTER
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2628 CN Delft
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06100 Nice
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M13 9PL Manchester
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Partners (2)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
94607 OAKLAND CA
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
15213 Pittsburgh
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