publications

Articles

• Journal

[9]   J. Clemens, T. Kluth, T. Reineking.  β-SLAM: Simultaneous Localization and Grid Mapping With Beta Distributions. Accepted for publication in Information Fusion, online available here ,2018

[8]   T. Kluth, B. Jin, G. Li.  On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging. In Inverse Problems, 34(9): 095006, 2018

[7]   T. Kluth.  Mathematical models for magnetic particle imaging. In Inverse Problems, 34(8): 083001, 2018

[6]   T. Kluth, P. Maass.  Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction. In International Journal on Magnetic Particle Imaging, Article ID 1707004, 3(2), 10 pages, 2017.

[5]   C. Bathke, T. Kluth, C. Brandt, P. Maass.  Improved image reconstruction in magnetic particle imaging using structural a priori information. In International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.

[4]   J. Clemens, T. Reineking, T. Kluth. An evidential approach to SLAM, path planning, and active exploration. In International Journal of Approximate Reasoning, 73:1-26, 2016.

[3]   T. Kluth, C. Zetzsche. Numerosity as a topological invariant. In Journal of Vision, 16(3):30, 1-39, 2016.

[2]  M. Gehre, T. Kluth, C. Sebu, P. Maass. Sparse 3D reconstructions in Electrical Impedance Tomography using real data. In Inverse Problems in Science and Engineering, 22(1):31-44, 2014.

[1]  M. Gehre, T. Kluth, A. Lipponen, B. Jin, A. Seppaenen, J. P. Kaipio, P. Maass. Sparsity Reconstruction in Electrical Impedance Tomography: An Experimental Evaluation. In Journal of Computational and Applied Mathematics,, 236(8):2126-2136, 2012.

• Conference proceedings (peer-reviewed)

[6]   T. Reineking, T. Kluth, D. Nakath. Adaptive information selection in images: Efficient naive bayes nearest neighbor classification. In G. Azzopardi and N. Petkov, editors, Computer Analysis of Images and Patterns, volume 9256 of Lecture Notes in Computer Science, pages 350-361. Springer International Publishing, 2015

[5]  T. Kluth, D. Nakath, T. Reineking, C. Zetzsche, K. Schill. Affordance-based object recognition using interactions obtained from a utility maximization principle. In Computer Vision-ECCV 2014 Workshops, volume 8926 of Lecture Notes in Computer Science, pages 406-412. Springer International Publishing, 2014.

[4]  T. Kluth, C. Zetzsche. Spatial numerosity: A computational model based on a topological invariant. In C. Freksa, B. Nebel, M. Hegarty, and T. Barkowsky, editors, Spatial Cognition IX, volume 8684 of Lecture Notes in Computer Science, pages 237-252. Springer International Publishing, 2014.

[3]  D. Nakath, T. Kluth, T. Reineking, C. Zetzsche, K. Schill. Active sensorimotor object recognition in three-dimensional space. In C. Freksa, B. Nebel, M. Hegarty, and T. Barkowsky, editors, Spatial Cognition IX, volume 8684 of Lecture Notes in Computer Science, pages 312-324. Springer International Publishing, 2014.

[2]  C. Zetzsche, K. Gadzicki, T. Kluth. Statistical Invariants of Spatial Form: From Local AND to Numerosity. In Proceedings of the Second Interdisciplinary Workshop The Shape of Things, pp. 163-172, 2013.

[1]  S. Eberhardt, T. Kluth, C. Zetzsche, K. Schill. From Pattern Recognition to Place Detection. In Proceedings of the International Workshop on Place-related Knowledge Acquisition Research (P-KAR), pp. 39-44, 2012.

Theses

[2]  T. Kluth. Intrinsic dimensionality in vision: Nonlinear filter design and applications. PhD thesis, 2015.

[1]  T. Kluth. 3D Electrical Impedance Tomography with Sparsity Constraints: Algorithm and implementation in application to the Complete Electrode Model. Diploma thesis, 2011.

Posters, talks and other scientific contributions

[23]  T. Kluth. The model-based reconstruction problem in magnetic particle imaging. Invited talk at International Conference on Sensing and Imaging, Liuzhou, China, 2018. Slides upon request.

[22]  T. Kluth. Spatio-temporal concentration estimation in magnetic particle imaging using a priori motion information. Invited talk at IFIP TC 7 Conference on System Modelling and Optimization, Essen, Germany, 2018. Slides upon request.

[21]  T. Kluth. Modeling of magnetic particle imaging. Invited talk at Universitaet Hamburg-Harburg (invited by Prof. Dr. Christina Brandt), Hamburg, Germany, 2018. Slides upon request.

[20]  T. Kluth. Mathematical models in magnetic particle imaging. Talk at SIAM Conference on Imaging Science, Bologna, Italy, 2018. Slides upon request.

[19]  T. Kluth, B. Jin. Exploiting ill-posedness in magnetic particle imaging - system matrix approximation via randomized SVD. Talk at 8th International Workshop on Magnetic Particle Imaging, Hamburg, Germany, 2018. Slides upon request.

[18]  T. Kluth. Modeling of magnetic particle imaging. Invited tutorial talk at 8th International Workshop on Magnetic Particle Imaging (invited by Prof. Dr. Thorsten Buzug, Prof. Dr. Tobias Knopp), Hamburg, Germany, 2018. Slides upon request.

[17]  T. Kluth. Model-based magnetic particle imaging. Invited talk at WIAS Berlin (invited by Prof. Dr. Michael Hintermueller), Berlin, Germany, 2018. Slides upon request.

[16]  T. Kluth. Model-based magnetic particle imaging. Invited talk at Oberseminar Wissenschaftliches Rechnen, Julius-Maximilians-Universitaet Wuerzburg (invited by Prof. Dr. Bernadette Hahn), Wuerzburg, Germany, 2017. Slides upon request.

[15]  T. Kluth. Operator uncertainty in model-based magnetic particle imaging. Invited talk at Inverse Problems Seminar, University College London (invited by Dr. Bangti Jin), London, UK, 2017. Slides upon request.

[14]  T. Kluth. Nonlinear parameter identification problems for model-based magnetic particle imaging (joint work with P. Maass). Talk at 9th Applied Inverse Problems Conference, Hangzhou, China, 2017. Slides upon request.

[13]  T. Kluth, P. Maass. Model Uncertainty in Magnetic Particle Imaging. Talk at the 7th International Workshop on Magnetic Particle Imaging, Prague, 2017. Slides upon request.

[12]  C. Bathke, T. Kluth, C. Brandt, P. Maass. Magnetic Particle Imaging: Improved image reconstruction using structural a priori information. Talk at the 22nd Inverse Days, Kuopio, 2016. Slides upon request.

[11]  T. Kluth. 3D electrical impedance tomography with sparsity constraints. Invited talk at the workshop Compressive Sensing and Sparsity: Theory and Applications in Tomography, Manchester, 2015.

[10]   T. Reineking, T. Kluth, D. Nakath. Adaptive information selection in images: Efficient naive bayes nearest neighbor classification. Poster at 16th International Conference on Computer Analysis of Images and Patterns, Valletta, 2015.

[9]  T. Kluth. Intrinsic dimensionality in vision: Nonlinear filter design and applications. Talk at my Doctorate Defense, Bremen, 2015.

[8]  T. Kluth, C. Zetzsche. Spatial numerosity: A computational model based on a topological invariant. Talk at International Conference on Spatial Cognition, Bremen, 2014.

[7]  T. Kluth, C. Zetzsche. Visual numerosity: A computational model based on a topological invariant. Poster at ECVP, Belgrade, 2014.

[6]  T. Kluth, C. Zetzsche. Numerical cognition in vision based on a topological invariant. Poster at OCCAM, Osnabrueck, 2014.

[5]  T. Kluth, D. Nakath, T. Reineking, C. Zetzsche, K. Schill. Sensorimotor integration using an information gain strategy in application to object recognition tasks. Poster at ECVP, Bremen, 2013.

[4]  S. Eberhardt, T. Kluth, C. Zetzsche, K.Schill. Slow features encode high-level concepts on HMAX outputs. Poster at OCCAM, Osnabrueck, 2013.

[3]  S. Eberhardt, T. Kluth, T. Reineking, C. Zetzsche, K. Schill. Models for invariant place recognition. Abstract in Proceedings of KogWis, p. 10, 2012.

[2]  S. Eberhardt, T. Kluth, M. Fahle, C. Zetzsche. The role of nonlinearities in hierarchical feed-forward models for pattern recognition. Poster at ECVP, Sardinia, 2012.

[1]  T. Kluth, S. Eberhardt, M. Fahle, C. Zetzsche. Slow features between invariance and selectivity. Poster at OCCAM, Osnabrueck, 2012.