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Jörg Lücke and Julian Eggert , "Expectation truncation and the benefits of preselection in training generative models", JMLR, vol. 11, pp. 2855-2900, 2010.
AbstractWe show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (EM) and uses an efficiently computable approximation to the sufficient statistics of a given model. The computational cost to compute the sufficient statistics is strongly reduced by selecting, for each data point, the relevant hidden causes. The approximation is applica...
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Alexander Gepperth, Sven Rebhan, Stephan Hasler, Jannik Fritsch , "Biased competition in visual processing hierarchies: a learning approach using multiple cues", Cognitive Computation, 2010.
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Nils Einecke and Julian Eggert , "Evaluation of Direct Plane Fitting for Depth and Parameter Estimation", Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA 2010, 2010.
AbstractRecently, a model-based depth estimation technique has been proposed, which estimates surface model parameters by means of Hooke-Jeeves optimization. Assuming a parametric surface model, the parameters best explaining the perspective changes of the surface between different views are estimated. This constitutes a fitting of models directly into stereo images, which is in contrast to the usual approach of fitting models into pre-processed disparit...
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Katrin Lohan, Sebastian Gieselmann, Anna-Lisa Vollmer, Katharina Rohlfing, Britta Wrede, Jannik Fritsch , "Does embodiment affect tutoring behavior?", Proc. IEEE 9th Int Development and Learning (ICDL) Conf, 2010.
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Miranda Grahl, Frank Joublin, Franz Kummert , "A method for visual model learning during tracking", Int. Conf. on Neural Information Processing (ICONIP), 2010.
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Volker Willert and Julian Eggert , "Dynamic Visual Motion Estimation", Probabilistic Methods for Motion Extraction (tentative), 2010.
AbstractVisual motion is the projection of scene movements on a visual sensor. It is a rich source of information for the analysis of a visual scene. Especially for dynamic vision systems the estimation of visual motion is important because it allows to deduce the motion of objects as well as the self-motion of the system relative to the environment. Therefore, visual motion serves as a basic information for navigation and exploration tasks, like obstacl...
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Gökhan Ince, Kazuhiro Nakadai, Tobias Rodemann, Hiroshi Tsujino, Jun-ichi Imura , "Multi-talker Speech Recognition under Ego-motion Noise using Missing Feature Theory", IEEE-RSJ International Conference on Intelligent Robot and Systems (IROS 2010), 2010.
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Alexander Denecke, Irene Ayllón Clemente, Heiko Wersing, Julian Eggert, Jochen Steil , "Figure-ground Segmentation using Metrics Adaptation in Level Set Methods", European Symposium on Artificial Neural Networks (ESANN), 2010.
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Chen Zhang and Julian Eggert , "Exploiting Hierarchical Prediction Models for Mixed 2D-3D Tracking", European Symposium on Artificial Neural Networks (ESANN), 2010.
AbstractIn this paper, we present a generic way to use a hierarchical representation of prediction models for adaptive tracking. Starting with a basic appearance-based tracker working in 2D retinal space, we show how to combine individual trackers for the left and right eye to a true 3D tracker that is built on top of the 2D trackers. We show how the trackers benefit from the hierarchical structure by dynamical model switching depending on the reliabilit...
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Robert Kastner, Thomas Michalke, Jannik Fritsch, Christian Goerick , "A Self-Adaptive Approach for Curbstone/Roadside Detection based on Human-Like Signal Processing and Multi-Sensor Fusion", IEEE Intelligent Vehicles Symposium (IV), 2010.
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