390 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université Pierre-et-Marie-CurieFR16 papers
- University of PittsburghUS13 papers
- Centre National de la Recherche ScientifiqueFR9 papers
- Carnegie Mellon UniversityUS7 papers
- InsermFR4 papers
- Institut national de recherche en sciences et technologies du numériqueFR4 papers
- Sorbonne UniversitéFR4 papers
- The University of Texas Southwestern Medical CenterUS4 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS3 papers
- Harvard UniversityUS3 papers
- Institut de Mathématiques de Jussieu-Paris Rive GaucheFR3 papers
- Massachusetts General HospitalUS3 papers
5 papers · 1 filter
Fitting ideals and multiple-points of surface parameterizations
Nicolás Botbol, Laurent Busé, Marc Chardin
Given a birational parameterization of an algebraic surface S in the projective space, the purpose of this paper is to investigate the sets of points on S whose preimage consists i…
Representations of 3-manifold groups in PGL(n,C) and their restriction to the boundary
Antonin Guilloux
We study here the space of representations of a fundamental group of a 3-manifold into PGL(n,C). Thurston, Neumann and Zagier initiated a strategy (in the case of PGL(2,C)) consist…
Discriminative Parameter Estimation for Random Walks Segmentation
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar +4
The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate…
Active polar fluid flow in finite droplets
Carl A. Whitfield, Davide Marenduzzo, Raphaël Voituriez +1
We present a continuum level analytical model of a droplet of active contractile fluid consisting of filaments and motors. We calculate the steady state flows that result from a sp…
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar +4
The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate…