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Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. [ 1 ]
One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input.
Information integration (II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations.It is used in data mining and consolidation of data from unstructured or semi-structured resources.
Image fusion methods can be broadly classified into two groups – spatial domain fusion and transform domain fusion. The fusion methods such as averaging, Brovey method, principal component analysis and IHS based methods fall under spatial domain approaches. Another important spatial domain fusion method is the high-pass filtering based technique.
The recognition-based fusion (also known as early fusion) consists in merging the outcomes of each modal recognizer by using integration mechanisms, such as, for example, statistical integration techniques, agent theory, hidden Markov models, artificial neural networks, etc. Examples of recognition-based fusion strategies are action frame, [54 ...
As such, it is a common sensor fusion and data fusion algorithm. Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for, all limit how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor ...
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
Data blending is a process whereby big data from multiple sources [1] are merged into a single data warehouse or data set. [ 2 ] Data blending allows business analysts to cope with the expansion of data that they need to make critical business decisions based on good quality business intelligence . [ 3 ]