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Shared office information appliances such as copiers, scanners and meeting-room displays are increasingly being integrated into complex electronic document workflows. To support this new role these appliances are being designed with advanced features such as optical character recognition, networked storage, content-based routing and integration with back-end databases, yet availability of these features...
A novel adaptive mixture-based neural network is presented for exploiting track data to learn normal patterns of motion behavior and detect deviations from normalcy. We have extended our prior approach by introducing multidimensional probability density components to represent class density using an adaptive mixture of such components. The number of components in the adaptive mixture algorithm, as...
Geospatial scene content understanding facilitates a large number of increasingly important applications. These range from tools to help intelligence analysts perform rapid, high-precision identification of urban scene content to other civilian and military security applications such as geospatial queries, functional object level change detection, and mission planning. In this paper, we present initial...
To date, our neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior have operated at fixed spatial scales. Although these models continuously adapted to incoming track data through incremental learning in order to adjust to evolving situations, the fundamental spatial scale of the learned...
An important component of higher level fusion and decision making is knowledge discovery. One form of knowledge representation is a set of probabilistic relationships between entities. Here we present biologically-inspired algorithmic support for automatic scene understanding and complex object recognition. Our algorithm learns the association between scene and complex objects and their primitive...
An improved neurobiologically inspired algorithm for situation awareness in the maritime domain takes real-time tracking information and learns motion pattern models based on temporal associations between vessel events enabling conditional probabilities between events to be learned incrementally and locally. These learned weights are used for future vessel location prediction. Improvements in prediction...
Contemporary situational awareness problems such as automated normalcy learning for anomaly detection and motion behavior prediction are addressed with biologically-inspired processing, representation, and learning approaches. Issues and challenges are discussed and our responses to them described. Relatively simple neural principles provide considerable power in providing capabilities required to...
An improved neurobiologically inspired algorithm for situation awareness in the maritime domain is presented, which takes real-time tracking information and learns motion pattern models on-the- fly, enabling the models to adapt well to evolving situations while maintaining high levels of performance. The constantly refined models, resulting from concurrent incremental learning, are used to evaluate...
SeeCoast is a prototype US Coast Guard (USCG) port surveillance system that provides automated scene understanding support for watchstanders. A major SeeCoast objective is to reduce operator workload while maintaining optimal domain awareness by shifting operators' focus from having to detect events to being able to analyze and act upon the knowledge derived from automatically detected anomalous activities...
This paper addresses maritime situation awareness by using cognitively inspired algorithms to learn behavioral patterns at a variety of conceptual, spatial, and temporal levels. The algorithms form the basis for a system that takes real-time tracking information and uses continuous on-the-fly learning that enables concurrent recognition of patterns of current motion states of single vessels in local...
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