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In this paper, we present a novel approach towards the integration of visual attention, object based attention and object recognition. Our system is scalable in regard to the required framerate or usage of computational power. Therefore, it is perfectly suited for robotic applications, where time is a crucial factor. We enhance and evaluate our previously presented visual attention system based on...
We propose a fast method for salient region detection which aims at providing a computationally efficient method for online image processing. It is scalable and can be adjusted on the run to adapt to different computational requirements, which makes it a perfect candidate for time crucial applications. In our approach, we apply a template sampling over the image and compare these templates with each...
In this paper, we present an biologically-motivated object recognition system for robots and vision tasks in general. Our approach is based on a hierarchical model of the visual cortex for feature extraction and rapid scene categorization. We modify this static model to be usable in time-crucial real-world scenarios by applying methods for optimization from signal detection theory, information theory,...
In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture’s focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations – making...
In this paper, we present a biologically-inspired object recognition system for humanoid robots. Our approach is based on a hierarchical model of the visual cortex for feature extraction and rapid scene categorization of natural images. We enhanced the model to be entropy-aware and real-time capable, to be able to realize object recognition over time. We integrate time in our system to model uncertainty...
This paper proposes a novel 3D scene interpretation approach for robots in mobile manipulation scenarios using a set of 3D point features (Fast Point Feature Histograms) and probabilistic graphical methods (Conditional Random Fields). Our system uses real time stereo with textured light to obtain dense depth maps in the robot's manipulators working space. For the purposes of manipulation, we want...
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