Sven Rebhan and Julian Eggert,
"Dynamic, Task-Related and Demand-Driven Scene Representation",
Cognitive Computation, 2010.
Abstract
Humans selectively process and store details about the vicinity based on their knowledge about the scene, the world and their current task. In doing so, only those pieces of information are extracted from the visual scene that is required for solving a given task. In this paper, we present a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approache...
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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.
Abstract
Belief 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 Heckmann, Claudius Gläser, Frank Joublin, Kazuhiro Nakadai,
"Applying Geometric Source Separation for Improved Pitch Extraction in Human-Robot Interaction",
Proc. INTERSPEECH, 2010.
Abstract
We present a system for robust pitch extraction in noisy and echoic environments consisting of a multi-channel signal enhancement, a pitch extraction algorithm inspired by the processing in the mammalian auditory system and a pitch tracking based on a Bayesian filter. For the multi-channel signal enhancement we deploy an 8 channel Geometric Source Separation (GSS). During pitch extraction we first apply a Gammatone filter bank and then calculate ...
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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.
Abstract
Humanoid 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.
Abstract
Humans 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.
Abstract
We 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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Jörg Lücke and Julian Eggert,
"Expectation truncation and the benefits of preselection in training generative models",
JMLR, vol. 11, pp. 2855-2900, 2010.
Abstract
We 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, Stephan Hasler, Sven Rebhan, Jannik Fritsch,
"Biased competition in visual processing hierarchies: a learning approach using multiple cues",
Cognitive Computation, 2010.
Abstract
In this contribution we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system, and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-dir...
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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.
Abstract
Recently, 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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