N
Nils Papenberg
Researcher at Fraunhofer Society
Publications - 30
Citations - 1242
Nils Papenberg is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Image registration & Landmark. The author has an hindex of 10, co-authored 30 publications receiving 1179 citations. Previous affiliations of Nils Papenberg include University of Lübeck.
Papers
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Proceedings Article
Highly accurate optic flow computation with theoretically justified warping
TL;DR: In this article, a variational model for optic flow computation based on non-linearised and higher order constancy assumptions is proposed, which is also capable of dealing with large displacements.
Journal ArticleDOI
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
TL;DR: A variational model for optic flow computation based on non-linearised and higher order constancy assumptions, including the common grey value constancy assumption, as well as the constancy of the Hessian and the Laplacian are proposed.
Journal ArticleDOI
3D ultrasound-CT registration of the liver using combined landmark-intensity information
Thomas Lange,Nils Papenberg,Stefan Heldmann,Jan Modersitzki,Bernd Fischer,Hans Lamecker,P.M. Schlag +6 more
TL;DR: A method of combining anatomical landmark information with a fast non-parametric intensity registration approach that improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks.
Book ChapterDOI
A survey on variational optic flow methods for small displacements
TL;DR: In this article, the authors present a survey on different model assumptions for each of these terms and illustrate their impact by experiments on rotationally invariant convex functionals with a linearised data term.
Proceedings ArticleDOI
A fully parallel algorithm for multimodal image registration using normalized gradient fields
Jan Rühaak,Lars König,Marc Hallmann,Nils Papenberg,Stefan Heldmann,Hanno Schumacher,Bernd M. Fischer +6 more
TL;DR: A super fast variational algorithm capable of registering full-body CT and PET images in about a second on a standard CPU with virtually no memory requirements, which is directly suitable for usage in many-core environments.