• DROP is a fast, efficient deformable registration platform for monomodal and multimodal fusion written in C++ for the medical imaging community (available here) developed at Ecole Centrale de Paris. The platform contains most of the existing metrics to perform registration under the same concept.
  • FastPD is a generic graph-based optimization platform written in C++ for the computer vision and medical imaging community (available here) developed at Ecole Centrale and University of Crete. This is the most efficient available platform in terms of a compromise of computational efficiency and ability to converge to a good minimum for the optimization of generic pair-wise MRFs.
  • GraPeS is a generic image parsing library based on re-inforcement learning written in C++ (available here) developed at Ecole Centrale de Paris. It can handle grammars (binary-split, four-color, Hausmannian) and image-based rewards (Gaussian mixtures, Randomized Forests) of varying complexity while being modular and computationally efficient both in terms of grammar and image rewards.