J
Jan Lellmann
Researcher at University of Lübeck
Publications - 70
Citations - 1614
Jan Lellmann is an academic researcher from University of Lübeck. The author has contributed to research in topics: Convex optimization & Total variation denoising. The author has an hindex of 18, co-authored 62 publications receiving 1418 citations. Previous affiliations of Jan Lellmann include Karlsruhe Institute of Technology & Heidelberg University.
Papers
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Proceedings ArticleDOI
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems
Jörg Hendrik Kappes,Bjoern Andres,Fred A. Hamprecht,Christoph Schnörr,Sebastian Nowozin,Dhruv Batra,Sungwoong Kim,Bernhard X. Kausler,Jan Lellmann,Nikos Komodakis,Carsten Rother +10 more
TL;DR: This study presents an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications and suggests that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.
Journal ArticleDOI
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
Jörg Hendrik Kappes,Bjoern Andres,Fred A. Hamprecht,Christoph Schnörr,Sebastian Nowozin,Dhruv Batra,Sungwoong Kim,Bernhard X. Kausler,Thorben Kröger,Jan Lellmann,Nikos Komodakis,Bogdan Savchynskyy,Carsten Rother +12 more
TL;DR: In this article, the authors present an empirical comparison of 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision.
Journal ArticleDOI
Continuous Multiclass Labeling Approaches and Algorithms
Jan Lellmann,Christoph Schnörr +1 more
TL;DR: In this paper, convex relaxations of the image labeling problem on a continuous domain with regularizers based on metric interaction potentials are studied and a globally convergent Douglas-Rachford scheme is proposed for solving the nonsmooth discretized problem.
Book ChapterDOI
Convex Multi-class Image Labeling by Simplex-Constrained Total Variation
TL;DR: In this article, a novel functional based on a multidimensional total variation formulation was proposed for multi-class labeling, allowing for a broad range of data terms, and optimization is carried out in the operator splitting framework using Douglas-Rachford Splitting.
Proceedings ArticleDOI
Total Variation Regularization for Functions with Values in a Manifold
TL;DR: This paper proposes the first algorithm to solve variational problems which applies to arbitrary Riemannian manifolds and demonstrates that the proposed framework can be applied to variational models that incorporate chromaticity values, normal fields, or camera trajectories.