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start [2018/02/02 10:59]
vincent
start [2018/06/20 08:57] (current)
guilherme
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-====== Geometry, imaGes, learninG and alGorithms ======+====== Geometry, imaGes, learninGand alGorithms ======
  
 The research group **Geometry, imaGes, learninG and alGorithms** (G4) is a member of the [[http://​limos.isima.fr/​spip.php?​article9|Decision Support Models and Algorithms (MAAD)]] group. We are all part of the [[http://​limos.isima.fr/​spip.php?​article7|LIMOS lab]], in the Cézeaux campus of [[http://​uca.fr|Université Clermont Auvergne]], near Clermont Ferrand, France. The research group **Geometry, imaGes, learninG and alGorithms** (G4) is a member of the [[http://​limos.isima.fr/​spip.php?​article9|Decision Support Models and Algorithms (MAAD)]] group. We are all part of the [[http://​limos.isima.fr/​spip.php?​article7|LIMOS lab]], in the Cézeaux campus of [[http://​uca.fr|Université Clermont Auvergne]], near Clermont Ferrand, France.
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 ====== Short summary ====== ====== Short summary ======
-The research topics of this group are centered on n-dimensional data modeling and analysis, both from a methodological ​and an applicative point of view. The staff has interdisciplinary skills, and the projects ​developed in the group cover a wide range of scientific topics, at the boundaries of Computer Science and Geometry, Data Mining ​and Machine Learning, ​or data structures ​and applications.+The research topics of this group are centered on n-dimensional data modeling and analysis, ​from both methodology ​and application points ​of view. The team'​s ​interdisciplinary skills ​result in projects ​that cover a wide range of scientific topics ​including ​Geometry, Data MiningMachine Learning, ​Data Structures, ​and their interactions for several fields of application.
  
-Our research ​field include+For a brief description of our main research ​areas, we mention
  
-Machine ​Learning: several ​ aspects ​are addressed, from the design of kernel methods to manifold learning and deep learning. Current research include modeling and simulation of spatio-temporal variations and dynamics using manifolds, understanding of the GANs approaches and their link to Optimal Transport, Siamese network for multimodal learning and autoencoders or solving multiclass SVM at the cost of a binary one, dealing with indefinite kernels or large datasets.+  * **Machine ​learning**We address ​several ​ aspects ​of machine learning, from the design of kernel methods to manifold learning and deep learning. Current research include modeling and simulation of spatio-temporal variations and dynamics using manifolds, understanding of the GANs approaches and their link to Optimal Transport, Siamese network for multimodal learning and autoencoders or solving multiclass SVM at the cost of a binary one, and dealing with indefinite kernels or large datasets.
  
-Clustering methods, using fuzzy clustering algorithms, or  semi-supervised clustering, also referred to as constrained clustering. Such methods use background knowledge in order to improve the accuracy of the solution+  * **Clustering methods**: We study fuzzy clustering algorithms, or  semi-supervised clustering, also referred to as constrained clustering. Such methods use background knowledge in order to improve the accuracy of the solution.
  
-Digital ​Geometry, strongly ​related ​with many other topics ​going from geometry of numbers ​to computational geometry, discrete geometry, combinatorics ​or inverse problemsFor instance, we are especially interested ​nowadays ​in questions of separability from a lattice set and its complementary on the grid +  * **Digital ​geometry**: The field is closely ​related ​to many other topics ​such as geometry of numberscomputational geometry, discrete geometry, ​and combinatorics. ​We are especially interested in questions of separability from a lattice set and its complement, as well as the recognition of digital polytopes.
  
-Computational geometry and geometric ​approximation,​ where we either approximate distances (e.g., approximate nearest neighbor searching) or the size of the solution (e.g., maximum independent set of a unit disk graph). ​Current works include the search of dramatic improvements to the complexity of several approximation problems such as polytope membership, nearest neighbor searching, epsilon-kernel,​ and diameter of a point set, using a hierarchy of Macbeath regions+  * **Geometric approximation**:​ We resort to approximations to solve geometric ​problems that would be intractable otherwise. We can either approximate distances (e.g., approximate nearest neighbor searching) or the size of the solution (e.g., maximum independent set of a unit disk graph).
  
-Image and Video Processingaddressed from both a methodological (definition ​of image analysis methods in nD) and applicative points ​of view (tracking of ballistics in thermal videos of volcanoes, design of computer assisted maps  for visually impaired people,…)+  * **Computational geometry**: We design algorithms ​and data structures for numerous geometric problems related to range searchinggeometric graphs, and several other topics. We analyze that asymptotic complexity ​of these algorithms ​and also prove lower bounds for the complexity ​of the problems.
  
 +  * **Image and video processing**:​ We address both the methodological (definition of image analysis methods in n dimension) and the  application points of view (tracking of ballistics in thermal videos of volcanoes, design of computer assisted maps for visually impaired people, ...).
  
-International ​collaborations include ​Hong Kong University of Science and Technology ​(Hong Kong), University of Bergen ​(Norway), UCLAFSUOSUHouston Rice and Maryland Universities ​(USA)  Murdoch University and CSIRO (Australia),​ UCL and the Royal Observatory of Belgium ​(Belgium) or UDESC (Brazil). Among all national partners are LITIS (Rouen)LMV (Clermont-Ferrand) ​and Creatis (Lyon) labs.+National ​collaborations include: LITIS (Rouen), LMV (Clermont-Ferrand), INRIA Sophia-AntipolisCreatis (Lyon)IMT (Toulouse)LIFL (Lille) ​and IGN (Paris)
 + 
 +International collaborations include: 
 + 
 +  * Australia: ​Murdoch University and CSIRO 
 +  * Belgium: Université Catholique de Louvain ​and the Royal Observatory of Belgium 
 +  * Brazil: UDESC and UFRJ 
 +  * Hong Kong: Hong Kong University of Science and Technology 
 +  * Norway: University of Bergen 
 +  * USA: University of California Los Angeles, Florida State University, Ohio State University, Rice University, and University of Maryland
  
 ===== News ===== ===== News =====