Research Director CNRS at Ceremade, Université Paris-Dauphine. Grand Prix from the French Academy of Sciences, EADS Foundation, 2009. IEEE Fellow for contributions to computer vision technology for medical imaging, 2010. For many years, he has been editorial member of the Journal of Mathematical Imaging and Vision, Medical Image Analysis and Machine Vision and Applications.
Topics of interest
Computer vison, image processing, Geodesics, Partial Differential Equations, Variational Methods, machine learning
Project in Prairie
Laurent Cohen’s research will focus on variational methods, PDEs and ML for image analysis, like object segmentation, shape analysis and deformation, in 2D or 3D images or point clouds. Applications lie in geometric structures present in biomedical imaging, in collaboration with institutes, hospitals or industry. He will participate in the PSL AI graduate school.
In the past 35 years, my research has focused on variational methods and Partial Differential Equations for Image Analysis with Deformable models and geodesic methods. A large part of my research has been done for image segmentation and shape recognition with applications in biomedical imaging, in collaboration with industry or hospitals (15 PhD supervision, among 25, with applications to biomedical imaging). Current work involves various aspects of Machine Learning.
Geodesic Methods: Image Segmentation, Active Contours Revisited. Applications to Medical Imaging.
Deformable Models: Edge-based or Region-based active contours, curve and surface Reconstruction, Image Segmentation and Restoration.
Machine Learning: Object segmentation and recognition.