Chen Zhang and Julian Eggert,
"Tracking with Multiple Prediction Models",
Artificial Neural Networks - ICANN, 19th International Conference, pp. 855-864, 2009.
Abstract
In Bayesian-based tracking systems, prediction is an essential part of the framework. It models object motion and links the internal estimated motion parameters with sensory measurement of the object from the outside world. In this paper a Bayesian-based tracking system with multiple prediction models is introduced. The benefit of multiple model prediction is that each of the models has individual strengths suited for different situations. For ex...
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Andre Justus,
"Nutzung der Tiefeninformationen eines Lasersensors zur Erweiterung einer Fahrspuridentifikation fuer ein Fahrerassistenzsystem",
Bielefeld University, 2009.
Abstract
In new vehicles, increasingly more advanced driver assistance systems (ADASs) have been incorporated in order to assist the driver in dangerous situations. Through warnings and interventions, ADASs increase the safety of the driver and others using the road. At the moment, mainly cameras and radar sensors are used in ADASs for sensing the environment. Laser sensors may also be used and present an advantage to radar in that they can detect slow mo...
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Sven Rebhan and Julian Eggert,
"Consistent Modeling of Functional Dependencies along with World Knowledge",
Proceedings of the International Conference on Cognitive Information Systems Engineering (ICCISE), pp. 341-348, 2009.
Abstract
In this paper we propose a method for visual systems to consistently represent functional dependencies between different visual routines along with relational short- and long-term knowledge about the world. Here the visual routines are bound to visual properties of objects stored in the memory of the system. Furthermore the functional dependencies between the visual routines are seen as a graph also belonging to the object’s structure. This gra...
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Markus Olhofer, Thomas Bihrer, Stefan Menzel, Michael Fischer, Bernhard Sendhoff,
"Evolutionary Optimisation of an Exhaust Flow Element with Free Form Deformation",
Simulation for Innovative Design, Proceedings of the 4th EASC - 2009 European Automotive Simulation Conference, pp. 163-174, 2009.
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Thomas Michalke,
"Task-Dependent Scene Interpretation in Driver Assistance",
TU Darmstadt, 2009.
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Ben Jones, Yaochu Jin, Bernhard Sendhoff, Xin Yao,
"The evolutionary emergence of neural organization in a hydra-like animat",
The Bernstein Conference on Computational Neuroscience, 2009.
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Claudius Gläser, Frank Joublin, Christian Goerick,
"Intrinsically Regulated Self-Organization of Topologically Ordered Neural Maps",
Frontiers in Computational Neuroscience. Conference Abstract: Bernstein Conference on Computational Neuroscience, 2009.
Abstract
Dynamic field theory models the spatio-temporal evolution of activity within the cortex and has been successfully applied in various domains. However, the development of dynamic neural fields (DNFs) is only rarely explored. This is due to the fact that DNFs are sensible to the right balance between excitation and inhibition within the fields. Small changes to this balance will result in runaway excitation or quiescence. Consequently, learning mos...
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Christian Lang, Marc Hanheide, Manja Lohse, Heiko Wersing, Gerhard Sagerer,
"Feedback Interpretation based on Facial Expressions in Human-Robot Interaction",
Proc. IEEE RO-MAN, 2009.
Abstract
In everyday conversation besides speech people also communicate by means of nonverbal cues. Facial expressions are one important cue, as they can provide useful information about the conversation, for instance whether the interlocutor seems to understand or appears to be puzzled. Similarly, in human–robot interaction they also give feedback about the interaction situation. We present a Wizard of Oz user study in an object teaching scenario wher...
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Martijn Meeter, Rob Veldkampf, Yaochu Jin,
"Multiple memory stores and operant conditioning: A rationale for memory's complexity",
Brain and Cognition, vol. 69, no. 1, pp. 200–208, 2009.
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Florian Röhrbein, Julian Eggert, Edgar Körner,
"Child-friendly divorcing: Incremental Hierarchy Learning in Bayesian Networks",
Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN), 2009.
Abstract
The autonomous learning of concept hierarchies is still a matter of research. Here we present a learning schema for Bayesian networks which results in a nested structure of sub- and superclass relationships. It is based on so-called parent divorcing but exploits the similarity of all nodes involved as expressed by their connectivity pattern. If the procedure is applied to simple object-property pairings a nested taxonomic hierarchy emerges. We fu...
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