2021 - Fellow of the American Mathematical Society For contributions to graphical model identification and shape analysis with applications to machine learning, medical imaging and computational anatomy.
Geodesic, Large deformation diffeomorphic metric mapping, Mathematical analysis, Diffeomorphism and Computational anatomy are his primary areas of study. He has researched Geodesic in several fields, including Space, Image processing and Gradient descent. His research in Large deformation diffeomorphic metric mapping intersects with topics in White matter, Normalization, Transformation algorithm, Multi contrast and Voxel.
His Mathematical analysis research is multidisciplinary, incorporating elements of Flow, Geometry, Topology and Image registration. His Diffeomorphism research includes elements of Euler equations, Spline, Algebra, Vector field and Shape analysis. His research integrates issues of Failing heart, Group and Ventricular remodeling in his study of Computational anatomy.
His primary areas of study are Artificial intelligence, Diffeomorphism, Large deformation diffeomorphic metric mapping, Geodesic and Mathematical analysis. His Artificial intelligence study incorporates themes from Algorithm, Computer vision and Pattern recognition. His Diffeomorphism research includes themes of Image registration, Vector field and Shape analysis.
Laurent Younes combines subjects such as Voxel and Metric with his study of Large deformation diffeomorphic metric mapping. His Geodesic study deals with Computational anatomy intersecting with Flow. Laurent Younes interconnects Geometry and Optimal matching in the investigation of issues within Mathematical analysis.
Laurent Younes mainly focuses on Diffeomorphism, Shape analysis, Geometry, Pure mathematics and Geodesic. His Diffeomorphism study integrates concerns from other disciplines, such as Coordinate system, Simple, Invariant, Sobolev space and Vector field. His Shape analysis research incorporates elements of Computational anatomy, Training set and Theoretical computer science.
Artificial intelligence is closely connected to Pattern recognition in his research, which is encompassed under the umbrella topic of Computational anatomy. His work deals with themes such as Motion and Metric, which intersect with Pure mathematics. Laurent Younes has included themes like Unit interval and Algebra in his Geodesic study.
His main research concerns Diffeomorphism, Shape analysis, Geometry, Amygdala and Atrophy. His Diffeomorphism study combines topics in areas such as Ribbon, Vector field and Coordinate system. His studies in Shape analysis integrate themes in fields like Training set, Theoretical computer science, Pure mathematics, Sobolev space and Computational anatomy.
His Computational anatomy research is multidisciplinary, relying on both Large deformation diffeomorphic metric mapping and Optimal control. His Geometry research integrates issues from Structure, Skeleton and White matter. The various areas that Laurent Younes examines in his Atrophy study include Entorhinal cortex, Braak staging, Cognitive impairment and Anatomy.
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Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
M. Faisal Beg;Michael I. Miller;Alain Trouvé;Laurent Younes.
International Journal of Computer Vision (2005)
On the Metrics and Euler-Lagrange Equations of Computational Anatomy
Michael I. Miller;Alain Trouvé;Laurent Younes.
Annual Review of Biomedical Engineering (2002)
Shapes and Diffeomorphisms
Computable elastic distances between shapes
Siam Journal on Applied Mathematics (1998)
Group Actions, Homeomorphisms, and Matching: A General Framework
M. I. Miller;L. Younes.
International Journal of Computer Vision (2001)
Geodesic Shooting for Computational Anatomy
Michael I. Miller;Alain Trouvé;Laurent Younes.
Journal of Mathematical Imaging and Vision (2006)
Visual Turing test for computer vision systems
Donald Geman;Stuart Geman;Neil Hallonquist;Laurent Younes.
Proceedings of the National Academy of Sciences of the United States of America (2015)
Estimation and annealing for Gibbsian fields
Annales De L Institut Henri Poincare-probabilites Et Statistiques (1988)
Statistics on diffeomorphisms via tangent space representations.
M. Vaillant;M.I. Miller;L. Younes;A. Trouvé.
A metric on shape space with explicit geodesics
Laurent Younes;Peter W. Michor;Jayant M. Shah;David B. Mumford.
Rendiconti Lincei-matematica E Applicazioni (2008)
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