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Titlebook: Cognitive Vision Systems; Sampling the Spectru Henrik I. Christensen,Hans-Hellmut Nagel Book 2006 Springer-Verlag Berlin Heidelberg 2006 Co

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Discrete Semiconductor Devices,r images. Both camera actions and image processing methods are represented as concepts of a semantic network. Image processing methods comprise depth computation to find a table, generation of object hypotheses in an overview image, and object verification in a close-up view. Camera actions are pan,
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Monolithic Integrated Circuits,uch an approach is inadequate for building systems that accommodate the variety that is typical of human environments. In this paper we define a framework for context aware observation of human activity. A context in this framework is defined as a network of situations. A situation network is interp
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Integrated Circuit Fundamentals,o a truly cognitive system, is the ability to learn: to be able to build and use models constructed autonomously from sensory input. In this paper we overview a number of steps we have taken along the route to the construction of such a system, and discuss some remaining challenges.
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Integrated Circuit Fundamentals,o a truly cognitive system, is the ability to learn: to be able to build and use models constructed autonomously from sensory input. In this paper we overview a number of steps we have taken along the route to the construction of such a system, and discuss some remaining challenges.
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Semiconductor Device Physics and Simulationariant to changes in scale. Because the method employs scale invariant feature detectors, objects can be detected and classified independently of the scale of observation. Our method has low computational complexity and can easily be trained for robust detection and classification of different object classes.
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Semiconductor Device Reliabilitybe general to all vision techniques that intend to extract visual information out of low-SNR image data. It is exactly a strength of cognitive systems that they are able to cope with non-ideal situations. In this chapter we will deal with this problem.
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Component Burn-In: The Changing Attituden be used for hypothesis verification, to further improve recognition performance. Experimental results show the capacity of the approach to categorize and segment object categories as diverse as cars and cows.
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