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Stanimir Dragiev, Mark Toussaint, Michael Gienger , "Tactile Exploration and Grasping Unknown Objects by Actively Learning Gaussian Process Implicit Shape Potentials", Workshop of the Robotics Science and Systems Conference (RSS), 2013.
AbstractObject estimation is an example where (a) promising query points depend on observed ones, (b) acquisition of observations is not for free – neither w.r.t. time, nor energy – and (c) the purpose of estimation can range from obtaining a very exact model to model just enough to grasp it. This makes it an active learning scenario worth discussing. GPISP is a probabilistic shape representation which is able to capture the sensor uncertainty and...
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Martin Heracles , "Vision-Based Prediction of Human Driver Behavior in Urban Traffic Environments", Bielefeld University, University of Bielefeld, 2013.
AbstractWe address the problem of inferring the appropriate behavior of a human driver from visual information about urban traffic scenes. The visual information is acquired by an on-board camera that monitors the scene in front of the car, resulting in a video stream as seen by the driver. The appropriate behavior consists in the actions a responsible driver would typically perform in the depicted situations, including both longitudinal and lateral c...
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Maha Salem, Friederike Eyssel, Katharina Rohlfing, Stefan Kopp, Frank Joublin , "To Err is Human(-like): Effects of Robot Gesture on Perceived Anthropomorphism and Likability", Int J Soc Robot, 2013.
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Thomas Guthier, Volker Willert, Karel Kreuter, Andrea Schnall, Julian Eggert , "Non-negative Sparse Coding for Motion Extraction", Int. Joint Conf. on Neural Networks (IJCNN), 2013.
AbstractVisual motion is a rich source of information that is directly coupled to the underlying shape of a moving object. One way to describe motion is to use optical flow fields. Due to the aperture problem, dense optical flow estimation is an illconstraint problem, while sparse optical flow estimation looses the shape information of moving objects. Current estimation algorithms based on regularization or segmentation fail at surface deformation...
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Benjamin Metka, Mathias Franzius, Ute Bauer-Wersing , "Outdoor self-localization of a mobile robot using SFA", Int. Conf. on Neural Information Processing (ICONIP), vol. 8226, no. 1, pp. 249-256, 2013.
AbstractWe apply slow feature analysis (SFA) to the problem of selflocalization with a mobile robot. A similar unsupervised hierarchical model has earlier been shown to extract a virtual rat's position as slowly varying features by directly processing the raw, high dimensional views captured during a training run. The learned representations encode the robot's position, are orientation invariant and similar to cells in a rodent's hippocampus. Here...
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Thomas Guthier, Steve Gerges, Volker Willert, Julian Eggert , "Learning Associative Spatiotemporal Features with Non-Negative Sparse Coding", European Symposium on Artificial Neural Networks (ESANN), pp. 261-266, 2013.
AbstractMotion features based on optical flow are very powerful in tasks such as the recognition of human actions or gestures. Usually, they are combined with gradient information to form a set of spatiotemporal features. However, humans can recognize gestures and actions and thus derive the implied motion out of static images alone. We model this associative recognition within a learned hierarchy of non-negative sparse coding layers. In the first stage...
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Christian Vollmer, Horst-Michael Groß, Julian Eggert , "Learning Features for Activity Recognition with Shift-invariant Sparse Coding", Int. Conf. on Artificial Neural Networks (ICANN), pp. 367-374, 2013.
AbstractIn activity recognition, traditionally, features are chosen heuristically, based on explicit domain knowledge. Typical features are statistical measures, like mean, standard deviation, etc., which are tailored to the application at hand and might not t in other cases. However, Feature Learning techniques have recently gained attention for building approaches that generalize over di erent application domains. More conventional approaches, like Pr...
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Nils Einecke and Julian Eggert , "Stereo Image Warping for Improved Depth Estimation of Road Surfaces", Intelligent Vehicles Symposium (IV), pp. 189-194, 2013.
AbstractAccurate stereoscopic depth estimation, in particular of the road surface area, is one of several key technologies to improve Advanced Driver Assistance Systems (ADAS). One major problem is that the quality of the stereoscopic depth measurements of the road is often poor - which is mainly attributed to a lack of texture on the road surface. Especially for patch-matching stereo algorithms, the estimated depths look irregular and bumpy. In this pap...
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Sarah Bonnin, Thomas H Weisswange, Franz Kummert, Jens Schmüdderich , "Accurate Behavior Prediction on Highways Based on a Systematic Combination of Classifiers", Intelligent Vehicles Symposium (IV), pp. 242-249, 2013.
AbstractTo drive safe, a good driver will observe his surroundings, anticipate the actions of other traffic participants and then decide for a maneuver. But if a driver is inattentive or overloaded he may fail to include some relevant information. This can than lead to wrong decisions and potentially result in an accident. In order to assist a driver in his decision making, Advanced Driver Assistance Systems (ADAS) are becoming more and more popular in c...
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Edgar Körner, Andreas Knoblauch, Ursula Körner , "Autonomous situation understanding and self-referential learning of situation representations in a brain-inspired architecture", Int. Conf. on Cognitive Neurodynamics (ICCN), 2013.
AbstractMaking sense of a scene has been considered a problem of sensory analysis traditionally. Prediction is used to cope with combinatorial explosion of possible alternative interpretations of the sensory signals. However, high complexity and variability of natural scenes limit the use of sensory appearance based prediction dramatically. Brains of living beings seem to use a different strategy. Evolution discovered the power of storing an episode of s...
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