Context-Based Reasoning Using Ontologies to Adapt Visual Tracking in Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Guest Editorial: Agent-based information fusion
Information Fusion
Situation assessment for automatic lane-change maneuvers
IEEE Transactions on Intelligent Transportation Systems
SES-based ontological process for high level information fusion
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Expert Systems with Applications: An International Journal
Factory Templates for Digital Factories Framework
Robotics and Computer-Integrated Manufacturing
Improving pervasive application behavior using other users' information
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Hi-index | 0.00 |
Master cutting-edge Level 2 fusion techniques that help you develop powerful situation assessment services with eye-popping capabilities and performance with this trail-blazing resource. The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and emerging technologies like particle filtering, spatiotemporal clustering, net-centricity, agent formalism, and distributed fusion together with essential Level 1 techniques and Level 1/2 interactions. Moreover, it includes all the tools you need to design high-level fusion services, select algorithms and software, simulate performance, and evaluate systems with never-before effectiveness. The book explains the Bayesian, fuzzy, and belief function formalisms of data fusion and a review of Level 1 techniques, including essential target tracking methods. Further, it covers Level 2 fusion methods for applications such as target classification and identification, unit aggregation and ambush detection, threat assessment, and relationships among entities and events, and assessing their suitability and capabilities in each case. The book's detailed discussion of Level 1/2 interactions emphasizes particle filtering techniques as unifying methods for both filtering under Level 1 fusion and inferencing in models for Level 2 fusion. The book also describes various temporal modeling techniques including dynamic Bayesian networks and hidden Markov models, distributed fusion for emerging network centric warfare environments, and the adaptation of fusion processes via machine learning techniques. Packed with real-world examples at every step, this peerless volume serves as an invaluable reference for your research and development of next-generation data fusion tools and services.