campus

Esperienze

Il Dipartimento di Ingegneria dell’Informazione,Ingegneria Elettrica e Matematica Applicata (DIEM) vanta una consolidata relazione con l’ENSICAEN.
Numerosi sono infatti gli studenti ERASMUS, di dottorato e PostDoc che negli ultimi anni hanno viaggiato tra Francia e Italia, così come numerose sono le relazioni scientifiche oggi in essere.

Scopriamo qualcosa in più..

Double

Double diploma


Questi sono gli studenti che hanno aderito al programma.. Contattali e chiedi loro informazioni!

  • Giovanni De Santis (now in progress, ha aderito al programma a partire dall’a.a. 2013/2014)

 

Double

Studenti ERASMUS

Studenti uscenti (da UNISA a ENSICAEN)
  • Vincenzo De Notaris: A multi-camera tracking algorithm devised for business intelligence application in a shopping centre by visual data– Relatore: Prof. Mario Vento, Prof. Luc Brun, Correlatore: Ing. Alessia Saggese (2013)
  • Roberto Pacilio: Design of a business intelligence application in a shopping centre based on the characterisation of customer behaviour by visual data – Prof. Mario Vento, Prof. Luc Brun, Correlatore: Ing. Alessia Saggese (2013)
  • Benito Cappellania: Detection of anomalous human behaviours by graph based representation – Prof. Mario Vento, Prof. Luc Brun, Correlatore: Ing. Alessia Saggese (2014)
  • Andrea Iuliano: A kernel based approach for the recognition of human actions – Prof. Mario Vento, Prof. Luc Brun, Correlatore: Ing. Alessia Saggese (2014)
Studenti entranti (da ENSICAEN a UNISA)
  • Leboucher Thibault, ENSICAEN: An algorithm for action recognition based on string kernel – Relatore: Prof. Mario Vento, Correlatore: Ing. Alessia Saggese (2013)
  • Raphael Boris, ENSICAEN: A method for detecting audio events of interest – Relatore: Prof. Pasquale Foggia (2013)
  • Guillaume Nicolas,ENSICAEN: Audio event recognition based on HMM – Relatore: Prof. Pasquale Foggia (2013)
  • Gaetan Le Barbe, ENSICAEN: A method for segmenting HEp2 Cells – Relatore: Prof. Mario Vento, Correlatore: Ing. Alessia Saggese (2014)
  • Jean Lakomsky, ENSICAEN: A method for Tracking moving objects – Relatore: Prof. Mario Vento, Correlatore: Ing. Alessia Saggese (2014)

 

Double

Sandwich Ph.D.

Studenti uscenti (da UNISA a ENSICAEN)
  • Alessia Saggese: Detecting and indexing moving objects for Behavior Analysis by Video and Audio Interpretation – Supervisor: Prof. Mario Vento, Prof. Luc Brun
  • Vincenzo Carletti (in progress): Graph in biomedical applications – Supervisor: Prof. Mario Vento, Prof. Luc Brun

 

Double

Post Doc

Studenti entranti (da ENSICAEN a UNISA)
  • Benoit Gauzere (in progress): Tracking and Behavior analysis based on the semantic interpretation of the scene

 

Double

Pubblicazioni in comune tra UNISA e ENSICAEN

  • Luc Brun, Mario Vento: Preface. Pattern Recognition 39(4): 499-500 (2006);
  • Luc Brun, Mario Vento: Graph-Based Representations in Pattern Recognition, 5th IAPR InternationalWorkshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings Springer 2005;
  • L. Brun, D. Conte, P. Foggia, M. Vento, D. Villemin, Symbolic Learning vs. Graph Kernels: An Experimental Comparison in a Chemical Application. Proceedings of the 14th Conference on Advances in Databases and Information Systems. 2010.
  • L. Brun, D. Conte, P. Foggia, M. Vento. “People re-identification by Graph Kernels Methods”. International Workshop on Graph Based Representation, 2011.
  • L. Brun, D. Conte, P. Foggia, M. Vento. “A graph-kernel method for re-identification”. International Conference on Image Analysis and Recognition, 2011.
  • Luc Brun, Alessia Saggese, Mario Vento (2012): A clustering algorithm of trajectories for behaviour understanding based on string kernels. In: Proceedings of the Conference on Signal Image Technology & Internet Based Systems (SITIS), pp. 267–274, IEEE, 2012
  • L. Brun, A. Saggese, M. Vento (2013): Learning and classification of car trajectories in road video by string kernels. In: Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), pp. 709-714, 2013
  • D. Conte, J.Y. Ramel, N. Sidère, M. Muzzamil Luqman, B. Gaüzère, J. Gibert, L. Brun, M. Vento (2013): A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition. In: Graph-Based Representations in Pattern Recognition, pp. 81-90, Springer Berlin Heidelberg, 2013, ISBN: 978-3-642-38221-5.
  • HAcK: A system for the Recognition of Human Actions by Kernels of Visual Strings – L. Brun, G. Percannella, A. Saggese, M. Vento, IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014
  • A reliable string kernel based approach for solving queries by sketch – Luc Brun, Alessia Saggese, Mario Vento – IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014
  • Brun L., Saggese A., Vento M. (2014): Dynamic Scene Understanding for behavior analysis based on string kernels. In: Circuits and Systems for Video Technology, IEEE Transactions on, 2014, ISSN: 1051-8215.
  • An unsupervised graph based approach for detecting abnormal vehicles behaviors – Luc Brun, Benito Cappellania, Alessia Saggese, Mario Vento – IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014