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Thomas Hurtut
B.Ing. (CentraleSupélec), M.Ing. (Poly), Ph.D. (Télécom ParisTech/Poly), Postdoc (Inria)

Phone: (514) 340-4711 Ext. 7109 Fax: (514) 340-5139 Room: M-3113

Research interests and affiliations

Research interests
  • Data visualization
  • Information visualization
  • 2D computer graphics
  • Generative and descriptive modeling of images
  • Authoring tools for the stylization of image content
  • Expressive/Non-photorealistic rendering
Affiliation(s)
Expertise type(s) (NSERC subjects)
  • 2603 Computer vision
  • 2707 Graphics

Publications

Recent publications
Conference paper
Levesque, F., St-Germain, M., Piche, D., Gauvin, J.-F., Gagnon, M. & Hurtut, T. (2020). MusX: Online exploring and visualizing graph-based musical adaptations. Paper presented at the 36th IEEE International Conference on Data Engineering (ICDE 2020), Dallas, TX (pp. 1734-1737). Retrieved from https://doi.org/10.1109/ICDE48307.2020.00157
Journal article
Gutierrez, J., Rabin, J., Galerne, B. & Hurtut, T. (2020). On Demand Solid Texture Synthesis Using Deep 3D Networks. Computer Graphics Forum, 39(1), 511-530. Retrieved from https://doi.org/10.1111/cgf.13889
Conference paper
Bléron, A., Vergne, R., Hurtut, T. & Thollot, J. (2018). Motion-coherent stylization with screen-space image filters. Paper presented at the Joint Symposium on Computational Aesthetics and Sketch Based Interfaces and Modeling and Non-Photorealistic Animation and Rendering (EXPRESSIVE 2018), Victoria, BC (13 pages). Retrieved from https://doi.org/10.1145/3229147.3229163
Conference paper
Christophe, S., Duménieu, B., Masse, A., Hoarau, C., Ory, J., Bédif, M., Lecordix, F., Mellado, N., Turbet, J., Loi, H., Hurtut, T., Vanderhaeghe, D., Vergne, R. & Thollot, J. (2017). Expressive map design : OGC SLD/SE++ extension for expressive map styles. Paper presented at the 28th International Cartographic Conference (ICC 2017), Washington, D.C., USA (4 pages).

Supervision at Polytechnique

COMPLETED

  • Ph.D. Thesis (1)

    • Christodoulidis, A. (2017). Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach (Ph.D. Thesis, École Polytechnique de Montréal). Retrieved from https://publications.polymtl.ca/2578/
  • Master's Thesis (3)