Télécom ParisTech

Isabelle Bloch

Professeur

Isabelle Bloch

Formation

  • Thèse ENST, 1990,
  • Habilitation à Diriger des Recherches, Paris 5, 1995.

Enseignement

  • Traitement des images, reconnaissance des formes, vision par ordinateur, fusion d’informations, théories de l’incertain, intelligence artificielle et image, imagerie médicale.

Thématiques de recherche

  • Traitement d’images 3D, interprétation sémantique des images, vision par ordinateur, morphologie mathématique 3D, floue et logique, fusion d’informations, théorie des ensembles flous, reconnaissance de structures dans les images par des approches structurelles (graphes, à base de connaissances...), raisonnement spatial, imagerie médicale.

Expérience industrielle et recherche contractuelle

  • Partenariats réguliers avec des entreprises dans le domaine de l’imagerie médicale (thèses en convention CIFRE, projets communs).

Animation scientifique et responsabilités collectives

  • Éditeur associé de revues internationales, comités de pilotage et de programme de conférences, expertises pour des projets nationaux et internationaux, membre du comité national du CNRS de 2008 à 2012, conseiller scientifique de l’ITMO Technologie pour la Santé.

Principaux Résultats scientifiques

  • I. Bloch, (2012), “Mathematical morphology on bipolar fuzzy sets: general algebraic framework”, International Journal of Approximate Reasoning, vol. 53, pp. 1031‑1061.
  • G. Fouquier, J. Atif et I. Bloch, (2012), “Sequential model‑based segmentation and recognition of image structures driven by visual features and spatial relations”, Computer Vision and Image Understanding, vol. 116, n° 1, pp. 146‑165.
  • J.P. de la Plata Alcalde, J. Anquez, L. Bibin, T. Boubekeur, E. D. Angelini et I. Bloch, (2011), “FEMONUM: A Framework for Whole Body Pregnant Woman Modeling from Ante‑Natal Imaging Data”, Eurographics 2011. Medical Prize Awards (Honorable Mention of the Dirk Bartz Prize for Visual Computing in Medicine 2011), vol. Medical Prize.
  • N. Widynski, S. Dubuisson et I. Bloch, (2012), “Fuzzy Spatial Constraints and Ranked Partitioned Sampling Approach for Multiple Object Tracking”, Computer Vision and Image Understanding, vol. 116, n° 10, pp. 1076‑1094.

Education

  • PhD thesis at ENST (former name of Télécom ParisTech) in 1990,
  • HDR at Paris 5 in 1995.

Teaching

  • Image processing, pattern recognition computer vision, information fusion, uncertainty theories, artificial intelligence and image, medical imaging.

Major Research Interest or Current Research Topics

  • 3-D image and object processing, semantic image interpretation, computer vision, 3-D, fuzzy and logics mathematical morphology, information fusion, fuzzy set theory, structural, graph-based and knowledge-based object recognition, spatial reasoning, and medical imaging.

Industrial Experience

  • Long-term partnership with medical imaging industry (PhD under CIFRE grants, joint projects).

Visibility, Membership, Committee

  • Associate editor for international journals, steering and program committees of conferences, expertises for national and international projects, member of the “Comité national du CNRS” (2008-2012), scientific expert in ITMO “Technologies for Health”.

Main Scientific Results

  • I. Bloch, (2012), “Mathematical morphology on bipolar fuzzy sets: general algebraic framework”, International Journal of Approximate Reasoning, vol. 53, pp. 1031‑1061.
  • G. Fouquier, J. Atif and I. Bloch, (2012), “Sequential model‑based segmentation and recognition of image structures driven by visual features and spatial relations”, Computer Vision and Image Understanding, vol. 116, n° 1, pp. 146‑165.
  • J.P. de la Plata Alcalde, J. Anquez, L. Bibin, T. Boubekeur, E. D. Angelini and I. Bloch, (2011), “FEMONUM: A Framework for Whole Body Pregnant Woman Modeling from Ante‑Natal Imaging Data”, Eurographics 2011. Medical Prize Awards (Honorable Mention of the Dirk Bartz Prize for Visual Computing in Medicine 2011), vol. Medical Prize.
  • N. Widynski, S. Dubuisson and I. Bloch, (2012), “Fuzzy Spatial Constraints and Ranked Partitioned Sampling Approach for Multiple Object Tracking”, Computer Vision and Image Understanding, vol. 116, n° 10, pp. 1076‑1094.