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— In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene graphs are extracted from image sequences and used to find the characteristic main graphs of the action sequence via an exact graph-matching technique, thus providing an event table(More)
—In mobile robotic applications, visual information needs to be processed fast despite resource limitations of the mobile system. Here a novel real-time framework for model-free spatio-temporal segmentation of stereo videos is presented. It combines real-time optical flow and stereo with image seg-mentation and runs on a portable system with an integrated(More)
Recognizing manipulations performed by a human and the transfer and execution of this by a robot is a difficult problem. We address this in the current study by introducing a novel representation of the relations between objects at decisive time points during a manipulation. Thereby, we encode the essential changes in a visual scenery in a condensed way(More)
Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as superpix-els, is a widely used preprocessing step in segmentation algorithms. Superpixel methods reduce the number of regions that must be considered later by more computation-ally expensive algorithms, with a minimal loss of information. Nevertheless, as some(More)
We present a real-time technique for the spatiotemporal segmentation of color/depth movies. Images are segmented using a parallel Metropolis algorithm implemented on a GPU utilizing both color and depth information, acquired with the Microsoft Kinect. Segments represent the equilibrium states of a Potts model, where tracking of segments is achieved by(More)
Efficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algorithm(More)
Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows(More)
We present an architecture for real-time, online vision systems which enables development and use of complex vision pipelines integrating any number of algorithms. Individual algorithms are implemented using modular plug-ins, allowing integration of independently developed algorithms and rapid testing of new vision pipeline configurations. The architecture(More)