@inproceedings{FeketeHallKoehleretal., author = {Fekete, S{\´a}ndor P. and Hall, Alexander and K{\"o}hler, Ekkehard and Kr{\"o}ller, Alexander}, title = {The Maximum Energy-Constrained Dynamic Flow Problem}, language = {en} } @inproceedings{BaierErlebachHalletal., author = {Baier, Georg and Erlebach, Thomas and Hall, Alexander and K{\"o}hler, Ekkehard and Schilling, Heiko and Skutella, Martin}, title = {Length-bounded cuts and flows}, language = {en} } @inproceedings{LiebchenWuenschKoehleretal., author = {Liebchen, Christian and W{\"u}nsch, Gregor and K{\"o}hler, Ekkehard and Reich, Alexander and Rizzi, Romeo}, title = {Benchmarks for strictly fundamental cycle bases}, language = {en} } @misc{BaierErlebachHalletal., author = {Baier, Georg and Erlebach, Thomas and Hall, Alexander and K{\"o}hler, Ekkehard and Schilling, Heiko and Skutella, Martin}, title = {Length-bounded cuts and flows}, language = {en} } @misc{HiltAliranguesNunezBakkeretal., author = {Hilt, Sabine and Alirangues Nu{\~n}ez, Marta M. and Bakker, Elisabeth S. and Blindow, Irmgard and Davidson, Thomas A. and Gillefalk, Mikael and Hansson, Lars-Anders and Janse, Jan H. and Janssen, Annette B. G. and Jeppesen, Erik and Kabus, Timm-Alexander and Kelly, Andrea and K{\"o}hler, Jan and Lauridsen, Torben L. and Mooij, Wolf M. and Noordhuis, Ruurd and Phillips, Geoff and R{\"u}cker, Jacqueline and Schuster, Hans-Heinrich and S{\o}ndergaard, Martin and Teurlincx, Sven and Weyer, Klaus van de and Donk, Ellen van and Waterstraat, Arno and Willby, Nigel and Sayer, Carl D.}, title = {Response of Submerged Macrophyte Communities to External and Internal Restoration Measures in North Temperate Shallow Lakes}, series = {Frontiers in plant science}, volume = {9}, journal = {Frontiers in plant science}, issn = {1664-462X}, doi = {10.3389/fpls.2018.00194}, pages = {24}, language = {en} } @misc{KoehlerBreuss, author = {K{\"o}hler, Alexander and Breuß, Michael}, title = {Towards Efficient Time Stepping for Numerical Shape Correspondence}, series = {Scale Space and Variational Methods in Computer Vision : 8th International Conference, SSVM 2021, Virtual Event, May 16-20, 2021, Proceedings}, journal = {Scale Space and Variational Methods in Computer Vision : 8th International Conference, SSVM 2021, Virtual Event, May 16-20, 2021, Proceedings}, editor = {Elmoataz, Abderrahim and Fadili, Jalal and Qu{\´e}au, Yvain and Rabin, Julien and Simon, Lo{\"i}c}, publisher = {Springer International Publishing}, isbn = {978-3-030-75548-5}, issn = {1611-3349}, doi = {10.1007/978-3-030-75549-2_14}, pages = {165 -- 176}, abstract = {The computation of correspondences between shapes is a principal task in shape analysis. To this end, methods based on partial differential equations (PDEs) have been established, encompassing e.g. the classic heat kernel signature as well as numerical solution schemes for geometric PDEs. In this work we focus on the latter approach. We consider here several time stepping schemes. The goal of this investigation is to assess, if one may identify a useful property of methods for time integration for the shape analysis context. Thereby we investigate the dependence on time step size, since the class of implicit schemes that are useful candidates in this context should ideally yield an invariant behaviour with respect to this parameter. To this end we study integration of heat and wave equation on a manifold. In order to facilitate this study, we propose an efficient, unified model order reduction framework for these models. We show that specific l0 stable schemes are favourable for numerical shape analysis. We give an experimental evaluation of the methods at hand of classical TOSCA data sets.}, language = {en} } @misc{KoehlerRigiBreuss, author = {K{\"o}hler, Alexander and Rigi, Ashkan and Breuß, Michael}, title = {Fast Shape Classification Using Kolmogorov-Smirnov Statistics}, series = {WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022}, journal = {WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022}, number = {CSRN 3201}, doi = {10.24132/CSRN.3201.22}, pages = {172 -- 180}, abstract = {The fast classification of shapes is an important problem in shape analysis and of high relevance for many possible applications. In this paper, we consider the use of very fast and easy to compute statistical techniques for assessing shapes, which may for instance be useful for a first similarity search in a shape database. To this end, we construct shape signatures at hand of stochastic sampling of distances between points of interest in a given shape. By employing the Kolmogorov-Smirnov statistics we then propose to formulate the problem of shape classification as a statistical hypothesis test that enables to assess the similarity of the signature distributions. In order to illustrate some important properties of our approach, we explore the use of simple sampling techniques. At hand of experiments conducted with a variety of shapes in two dimensions, we give a discussion of potentially interesting features of the method.}, language = {en} } @misc{KoehlerBreuss, author = {K{\"o}hler, Alexander and Breuß, Michael}, title = {Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation}, series = {Algorithms}, volume = {15}, journal = {Algorithms}, number = {9}, issn = {1999-4893}, doi = {10.3390/a15090304}, pages = {304}, abstract = {The computation of correspondences between shapes is a principal task in shape analysis. In this work, we consider correspondences constructed by a numerical solution of partial differential equations (PDEs). The underlying model of interest is thereby the classic wave equation, since this may give the most accurate shape matching. As has been observed in previous works, numerical time discretisation has a substantial influence on matching quality. Therefore, it is of interest to understand the underlying mechanisms and to investigate at the same time if there is an analytical model that could best describe the most suitable method for shape matching. To this end, we study here the damped wave equation, which mainly serves as a tool to understand and model properties of time discretisation. At the hand of a detailed study of possible parameters, we illustrate that the method that gives the most reasonable feature descriptors benefits from a damping mechanism which can be introduced numerically or within the PDE. This sheds light on some basic mechanisms of underlying computational and analytic models, as one may conjecture by our investigation that an ideal model could be composed of a transport mechanism and a diffusive component that helps to counter grid effects.}, language = {en} } @misc{KoehlerNikitinSonnenfeldetal., author = {K{\"o}hler, Marcel and Nikitin, Alexander and Sonnenfeld, Peter and Ossenbrink, Ralf and J{\"u}ttner, Sven}, title = {Wire arc additive manufacturing of aluminum foams using TiH2-Laced Welding Wires}, series = {Materials}, volume = {17}, journal = {Materials}, number = {13}, publisher = {MDPI AG}, issn = {1996-1944}, doi = {10.3390/ma17133176}, abstract = {Composite materials made from aluminum foam are increasingly used in aerospace and automotive industries due to their low density, high energy absorption capacity, and corrosion resistance. Additive manufacturing processes offer several advantages over conventional manufacturing methods, such as the ability to produce significantly more geometrically complex components without the need for expensive tooling. Direct Energy Deposition processes like Wire Arc Additive Manufacturing (WAAM) enable the additive production of near-net-shape components at high build rates. This paper presents a technology for producing aluminum foam structures using WAAM. This paper's focus is on the development of welding wires that are mixed with a foaming agent (TiH2) and produce a foamed weld metal as well as their processing using MIG welding technology.}, language = {en} } @misc{KoehlerBreuss, author = {K{\"o}hler, Alexander and Breuß, Michael}, title = {Towards Efficient Time Stepping for Numerical Shape Correspondence}, series = {arXiv}, journal = {arXiv}, doi = {10.48550/arXiv.2312.13841}, pages = {1 -- 12}, language = {en} } @misc{KoehlerBreussShabani, author = {K{\"o}hler, Alexander and Breuß, Michael and Shabani, Shima}, title = {Dictionary learning with the K-SVD algorithm for recovery of highly textured images : an experimental analysis}, series = {Proceedings of the Conference Algoritmy 2024}, journal = {Proceedings of the Conference Algoritmy 2024}, editor = {Frolkovič, P. and Mikula, K. and Ševčovič, D.}, publisher = {Jednota slovensk{\´y}ch matematikov a fyzikov}, address = {Bratislava}, isbn = {978-80-89829-33-0}, pages = {264 -- 273}, abstract = {Image recovery by dictionary learning is of potential interest for many possible applications. To learn a dictionary, one needs to solve a minimization problem where the solution should be sparse. The K-SVD formalism, which is a generalization of the K-means algorithm, is one of the most popular methods to achieve this aim. We explain the preprocessing that is needed to bring images into a manageable format for the optimization problem. The learning process then takes place in terms of solving for sparse representations of the image batches. The main contribution of this paper is to give an experimental analysis of the recovery for highly textured imagery. For our study, we employ a subset of the Brodatz database. We show that the recovery of sharp edges plays a considerable role. Additionally, we study the effects of varying the number dictionary elements for that purpose.}, language = {en} } @misc{KoehlerKahraBreuss, author = {K{\"o}hler, Alexander and Kahra, Marvin and Breuß, Michael}, title = {A First Approach to Quantum Logical Shape Classification Framework}, series = {Mathematics}, volume = {12}, journal = {Mathematics}, number = {11}, publisher = {MDPI AG}, address = {Basel}, issn = {2227-7390}, doi = {10.3390/math12111646}, abstract = {Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to devise suitable applications. This article aims to propose for the first time an approach using quantum logic in image processing for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way, we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification, we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables us to initiate the principal component analysis. Through experiments, we explore the possibility of distinguishing between different geometrical objects and discuss the results in terms of quantum logical interpretation.}, language = {en} } @misc{KoehlerBreussShabani, author = {K{\"o}hler, Alexander and Breuß, Michael and Shabani, Shima}, title = {Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images}, series = {Preprints.org}, journal = {Preprints.org}, publisher = {MDPI AG}, doi = {10.20944/preprints202406.0355.v1}, abstract = {Image recovery by dictionary learning is of potential interest for many possible applications. To learn a dictionary, one needs to solve a minimization problem where the solution should be sparse. The K-SVD formalism, which is a generalization of the K-means algorithm, is one of the most popular methods to achieve this aim. We explain the preprocessing that is needed to bring images into a manageable format for the optimization problem.The learning process then takes place in terms of solving for sparse representations of the image batches. The main contribution of this paper is to give an experimental analysis of the recovery for highly textured imagery. For our study, we employ a subset of the Brodatz database. We show that the recovery of sharp edges plays a considerable role. Additionally, we study the effects of varying the number dictionary elements for that purpose.}, language = {en} } @misc{KoehlerKahraBreuss, author = {K{\"o}hler, Alexander and Kahra, Marvin and Breuß, Michael}, title = {A First Approach to Quantum Logical Shape Classification Framework}, series = {Preprints.org}, journal = {Preprints.org}, publisher = {MDPI AG}, doi = {10.20944/preprints202402.1042.v1}, abstract = {Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.}, language = {en} } @misc{KoehlerBreuss, author = {K{\"o}hler, Alexander and Breuß, Michael}, title = {Recognition of geometrical shapes by dictionary learning}, series = {Proceedings of International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2025), 7-9 August 2025, Antalya-T{\"u}rkiye}, volume = {2025}, journal = {Proceedings of International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2025), 7-9 August 2025, Antalya-T{\"u}rkiye}, publisher = {IEEE}, address = {Piscataway, New Jersey}, isbn = {979-8-3315-3562-9}, doi = {10.1109/ACDSA65407.2025.11166282}, pages = {1 -- 6}, abstract = {Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks.}, language = {en} }