Renaud Keriven spends much of his time researching Artificial intelligence, Computer vision, Robustness, Point cloud and Surface. Renaud Keriven interconnects Geodesic and Graphics in the investigation of issues within Artificial intelligence. Renaud Keriven combines subjects such as Energy and Boundary with his study of Computer vision.
His Robustness research is multidisciplinary, incorporating elements of Motion estimation and Image registration. He has included themes like Real image and Variational principle in his Surface study. His work carried out in the field of Variational principle brings together such families of science as Algorithm, Level set and Partial differential equation.
Renaud Keriven mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Algorithm. His study in Segmentation, Robustness, Active shape model, Level set and Image processing falls under the purview of Artificial intelligence. His Level set study combines topics from a wide range of disciplines, such as Real image, Surface, Level set method and Variational principle.
The concepts of his Computer vision study are interwoven with issues in Polygon mesh and Geodesic. His Pattern recognition research includes elements of Nonlinear dimensionality reduction, Cognitive neuroscience of visual object recognition and Kernel. His biological study spans a wide range of topics, including Partial differential equation, Mathematical optimization and Inverse problem.
His main research concerns Artificial intelligence, Computer vision, Algorithm, Iterative reconstruction and Outlier. His work deals with themes such as Graph and Pattern recognition, which intersect with Artificial intelligence. In his work, Geometric primitive and Segmentation is strongly intertwined with Polygon mesh, which is a subfield of Computer vision.
His research in Algorithm intersects with topics in Image plane, Orientation, Graph theory and Mathematical optimization. The study incorporates disciplines such as Pipeline, Visibility and Surface reconstruction in addition to Iterative reconstruction. The Robustness study combines topics in areas such as Delaunay triangulation and Cut.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image segmentation, Point cloud and Iterative reconstruction. His Artificial intelligence study often links to related topics such as Graphics. As a part of the same scientific family, Renaud Keriven mostly works in the field of Computer vision, focusing on Geodesic and, on occasion, Voronoi diagram, Computer graphics, Surface and Riemannian manifold.
His studies in Image segmentation integrate themes in fields like Basis, Algorithm, Polygon mesh and Geometric primitive. Renaud Keriven has researched Point cloud in several fields, including Delaunay triangulation, Pipeline, Surface reconstruction, Mathematical optimization and Visibility. His study in Iterative reconstruction is interdisciplinary in nature, drawing from both Markov random field, Iterative method, Iterative refinement and 3D modeling.
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Variational principles, surface evolution, PDE's, level set methods and the stereo problem
O. Faugeras;R. Keriven.
5th IEEE EMBS International Summer School on Biomedical Imaging, 2002. (2002)
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
O. Faugeras;R. Keriven.
IEEE Transactions on Image Processing (1998)
CURVES: Curve evolution for vessel segmentation
L.M. Lorigo;O.D. Faugeras;O.D. Faugeras;W.E.L. Grimson;R. Keriven.
Medical Image Analysis (2001)
A common formalism for the Integral formulations of the forward EEG problem
J. Kybic;M. Clerc;T. Abboud;O. Faugeras.
IEEE Transactions on Medical Imaging (2005)
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
Jean-Philippe Pons;Renaud Keriven;Olivier Faugeras.
International Journal of Computer Vision (2007)
Towards high-resolution large-scale multi-view stereo
Vu Hoang Hiep;Renaud Keriven;Patrick Labatut;Jean-Philippe Pons.
computer vision and pattern recognition (2009)
Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts
P. Labatut;J.-P. Pons;R. Keriven.
international conference on computer vision (2007)
Complete Dense Stereovision Using Level Set Methods
Olivier D. Faugeras;Renaud Keriven.
european conference on computer vision (1998)
High Accuracy and Visibility-Consistent Dense Multiview Stereo
H-H Vu;P. Labatut;J-P Pons;R. Keriven.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics
Guillaume Charpiat;Olivier Faugeras;Renaud Keriven.
Foundations of Computational Mathematics (2005)
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