Heiko Neumann

Affiliations: 
Institute of Neural Information Processing  Ulm University, Ulm, Baden-Württemberg, Germany 
Area:
Neural models of visual information processing, computational vision and learning
Website:
http://www.informatik.uni-ulm.de/ni/staff/HNeumann/
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"Heiko Neumann"
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Publications

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Gomez O, Neumann H. (2016) Biologically Inspired Model for Inference of 3D Shape from Texture. Plos One. 11: e0160868
Hochdorfer S, Neumann H, Schlegel C. (2016) Landmark rating and selection for SLAM in dynamic environments Advances in Intelligent Systems and Computing. 302: 401-414
Abdul-Kreem LI, Neumann H. (2015) Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System. Plos One. 10: e0142488
Brosch T, Neumann H, Roelfsema PR. (2015) Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks. Plos Computational Biology. 11: e1004489
Schrodt F, Layher G, Neumann H, et al. (2015) Embodied learning of a generative neural model for biological motion perception and inference. Frontiers in Computational Neuroscience. 9: 79
Brosch T, Tschechne S, Neumann H. (2015) On event-based optical flow detection. Frontiers in Neuroscience. 9: 137
Paul J, Wundrak S, Bernhardt P, et al. (2015) Self-gated tissue phase mapping using golden angle radial sparse SENSE. Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine
Paul J, Beck R, Buckert D, et al. (2015) Left ventricular motion quantification parameters from tissue phase mapped MRI: Influence of gender Journal of Cardiovascular Magnetic Resonance. 1-4
Layher G, Tschechne S, Niese R, et al. (2015) Towards the Separation of Rigid and Non-rigid Motions for Facial Expression Analysis Proceedings - 2015 International Conference On Intelligent Environments, Ie 2015. 176-179
Medathati NVK, Neumann H, Masson GS, et al. (2015) Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision Computer Vision and Image Understanding. 150: 1-30
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