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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, algorithm which tries to compute the best scoring instance of a goal concept. The sequence of actions for computing an instance is determined by precedences which are either adjusted manually or computed from reinforcement-learning. Results are presented comparing the two approaches.
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Monolithic Integrated Circuits,ing systems for observing activity. We discuss methods for building systems using this framework. The framework and methods are illustrated with examples from observation of human activity in an “Augmented Meeting Environment”.
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Integrating Video Information over Time. Example: Face Recognition from Videobe 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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Interleaving Object Categorization and Segmentationn 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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Things That See: Context-Aware Multi-modal Interactioning systems for observing activity. We discuss methods for building systems using this framework. The framework and methods are illustrated with examples from observation of human activity in an “Augmented Meeting Environment”.
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On Scene Interpretation with Description Logicsnd spatial constraints. Reasoning services of the DL system can be used as building blocks for the interpretation process, but additional information is required to generate preferred interpretations. A probabilistic model is sketched which can be integrated with the knowledge-based framework.
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