Dr.-Ing. Karsten Vogt
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Karsten Vogt left the Institut für Informationsverabeitung.
Publications and research activities from the time after the departure are not listed here.

Karsten Vogt studied Computer Science at the Leibniz University of Hannover. He wrote his Master Thesis on the topic "Detektion regelmäßiger Strukturen durch Level-Set-Segmentierung". Since January of 2010 he has been working toward a PhD degree at the Institut für Informationsverarbeitung which he finished in 2018 on the topic of "Bayessches Transferlernen für die Semantische Segmentierung von Luftbildern". His research interests are machine learning and image processing on remote sensing data with a focus on transfer learning, Markov-Random-Field (MRF) modeling and Markov-Chain-Monte-Carlo (MCMC) sampling methods.

Show recent publications only
  • Conference Contributions
    • Karsten Vogt, Andreas Paul, Franz Rottensteiner, Jörn Ostermann, Christian Heipke
      Boosted Unsupervised Multi-Source Selection for Domain Adaptation
      ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol. 4, June 2017
    • Karsten Vogt, Jörn Ostermann
      Soft Margin Bayes-Point-Machine Classification via Adaptive Direction Sampling
      Scandinavian Conference on Image Analysis, Springer, pp. 313-324, June 2017
    • Ulrike Pestel-Schiller, Karsten Vogt, Jörn Ostermann, Wolfgang Groß
      Impact of Hyperspectral Image Coding on Subpixel Detection
      Proceedings of 32nd Picture Coding Symposium, Nürnberg, December 2016
    • Karsten Vogt, Oliver Müller, Jörn Ostermann
      Facial Landmark Localization using Robust Relationship Priors and Approximative Gibbs Sampling
      Advances in Visual Computing , Springer, Vol. 9475, pp. 365 -- 376, Las Vegas, December 2015, edited by George Bebis et al.
    • Florian Baumann, Jinghui Chen, Karsten Vogt, Bodo Rosenhahn
      Improved Threshold Selection by using Calibrated Probabilities for Random Forest Classifiers
      12th Conference on Computer and Robot Vision (CRV), Halifax, Nova Scotia, Canada, June 2015
    • Florian Baumann, Karsten Vogt, Arne Ehlers, Bodo Rosenhahn
      Probabilistic Nodes for Modelling Classification Uncertainty for Random Forest
      14th IAPR International Conference on Machine Vision Applications (MVA), Tokio, Japan, May 2015
    • Florian Baumann, Arne Ehlers, Karsten Vogt, Bodo Rosenhahn
      Cascaded Random Forest for Fast Object Detection
      18th Scandinavian Conference on Image Analysis (SCIA), Espoo, Finland, June 2013
    • Petra Helmholz, Christian Becker, Uwe Breitkopf, Torsten Büschenfeld, Andreas Busch, Dietmar Grünreich, Christian Heipke, Sönke Müller, Jörn Ostermann, Martin Pahl, Karsten Vogt, Marcel Ziems
      Semiautomatic Quality Control of Topographic Reference Datasets
      ISPRS Commission 4 Symposium, Orlando, Florida, November 2010
    • Karsten Vogt, Björn Scheuermann, Christian Becker, Torsten Büschenfeld, Bodo Rosenhahn, Jörn Ostermann
      Automated Extraction of Plantations from Ikonos Satellite Imagery using a Level Set Based Segmentation Method
      ISPRS Technical Commission VII Symposium, International Society for Photogrammetry and Remote Sensing, Vol. 38, No. 7, pp. 275-280, 2010
  • Journals
    • Karsten Vogt, Andreas Paul, Jörn Ostermann, Franz Rottensteiner, Christian Heipke
      Unsupervised Source Selection for Domain Adaptation
      Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 84, No. 5, pp. 249--261, May 2018
    • HA Haenssle, L. Hofmann, M Schoen, T Werfel, A Guenther, C Basu, B Roth, K-H Niemann, O Schmerling, S Scharenberg, F Luellau, K Vogt, B Rosenhahn, M Meinhardt-Wollenweber, S Emmert
      Automated Skin Cancer-Screening with contact free" Remote-Dermatoscopy"
    • Petra Helmholz, Christian Becker, Uwe Breitkopf, Torsten Büschenfeld, Andreas Busch, Carola Braun, Dietmar Grünreich, Sönke Müller, Jörn Ostermann, Martin Pahl, Franz Rottensteiner, Karsten Vogt, Marcel Ziems, Christian Heipke
      Semi-automatic Quality Control of Topographic Data Sets
      Photogrammetric Engineering & Remote Sensing, Vol. 78, No. 9, pp. 959--972, September 2012