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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 ]
Project Maven (officially Algorithmic Warfare Cross Functional Team) is a Pentagon project involving using machine learning and data fusion to process data from many sources, identify potential targets, display information through a user interface, and transmit human decisions to weapon systems, among other functions. It began in 2017.
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. Sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion.
Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty. [ 1 ] Examples of technologies available to integrate information include deduplication , and string metrics which allow the detection of similar text in different data sources by fuzzy ...
The purpose of image fusion is not only to reduce the amount of data but also to construct images that are more appropriate and understandable for the human and machine perception. [1] [2] In computer vision, multisensor image fusion is the process of combining relevant information from two or more images into a single image. [3]
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 ]
Fusion of the biometrics information can occur at different stages of a recognition system. In case of feature level fusion, the data itself or the features extracted from multiple biometrics are fused. Matching-score level fusion consolidates the scores generated by multiple classifiers pertaining to different
Hank Asher (May 9, 1951 – January 11, 2013) [1] was an American businessman who founded several data fusion and data mining companies that compile information about companies, individuals and their interrelationships from thousands of different electronic databases. [2] [3] He was known by industry insiders as "the father of data fusion." [4] [5]