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Stefan Menzel, Markus Olhofer, Bernhard Sendhoff , "A Metamodel-driven Interactive Framework for a Designer Assistance System", Proceedings of the 11th International Design Conference – Design 2010, pp. 1371–1380, 2010.
AbstractThe design of innovative products in the automotive industry is influenced by multiple criteria dominated by both human creativity and technical requirements. Thus the generation of a prototype is an adaptive process which iteratively integrates the needs of various disciplines, working on different timescales. This paper proposes and evaluates a styling design framework which introduces the application of neural networks for fast estimation of t...
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Martin Ernst Heckmann , "Supervised vs. Unsupervised Learning of Spectro Temporal Speech Features", Statistical And Perceptual Audition (SAPA), 2010.
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Robert Kastner, Thomas Michalke, Thomas Burbach, Jannik Fritsch, Christian Goerick , "Attention-Based Traffic Sign Recognition with an Array of Weak Classifiers", IEEE Intelligent Vehicles Symposium (IV), 2010.
AbstractCurrently available traffic sign recognition systems typically focus on a single class of traffic sign and therefore, the algorithms are optimized to find only this specific class. To this end, a number of approaches for real time capable classification of mostly circular signs exist. Nevertheless, to simultaneously recognize a number of classes a different way has to be taken. This paper presents a real-time capable approach, which uses a two-ti...
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Sven Rebhan and Julian Eggert , "Dynamic, Task-Related and Demand-Driven Scene Representation", Cognitive Computation, 2010.
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Volker Willert and Julian Eggert , "Belief Propagation in Spatiotemporal Graph Topologies for the Analysis of Image Sequences", Proceedings of the VISAPP, 2010.
AbstractBelief Propagation (BP) is an efficient approximate inference technique both for Markov Random Fields (MRF) and Dynamic Bayesian Networks (DBN). 2DMRFs provide a unified framework for early vision problems that are based on static image observations. 3D MRFs are suggested to cope with dynamic image data. To the contrary, DBNs are far less used for dynamic low level vision problems even though they represent sequences of state variables and hence ...
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Martin Ernst Heckmann, Frank Joublin, Kazuhiro Nakadai , "Applying Geometric Source Separation for Improved Pitch Extraction in Human-Robot Interaction", Proc. INTERSPEECH, 2010.
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Michael Gienger, Mark Toussaint, Christian Goerick , "Whole-body Motion Planning – Building Blocks for Intelligent Systems", Motion Planning for Humanoid Robots, Springer, issue 1st Edition, 2010.
AbstractHumanoid robots have become increasingly sophisticated, both in terms of their movement as well as their sensorial capabilities. This allows one to target for more challenging problems, eventually leading to robotic systems that can perform useful tasks in everyday environments. In this paper, we review some elements we consider to be important for a movement control and planning architecture. We first explain the whole-body control concept, whic...
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Stephan Hasler , "Learning Features for Robust Object Recognition", Honda Research Institute Europe GmbH / Bielefeld University, 2010.
AbstractHumans can easily recognize a very large number of previously seen objects. Despite extensive efforts in recent years, the principles underlying this capability are hardly understood and modern object recognition systems are far from reaching human performance. The main reason for this is that the visual appearance of an object is strongly influenced by various conditions. So depending on the viewing angle different parts of an object are vis...
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Andreas Knoblauch , "Bimodal structural plasticity can explain the spacing effect in long-term memory tasks.", Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience, 2010.
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Matthias Rolf, Jochen Steil, Michael Gienger , "Goal Babbling permits direct learning of inverse kinematics", IEEE Transactions on Autonomous Mental Development, vol. 2, no. 3, pp. 216 - 229, 2010.
AbstractWe present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path based sampling approach: we generate trainig data along pathes, which result from execution of the currently learned estimate along a desired path towards a goal. The information structure thereby induced ...
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