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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 ]
Sensor fusion is a process of combining sensor data or data derived from disparate sources so that the resulting information has less uncertainty than would be possible if these sources were used individually.
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]
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
Image fusion based on the multi-scale transform is the most commonly used and promising technique. Laplacian pyramid transform, gradient pyramid-based transform, morphological pyramid transform and the premier ones, discrete wavelet transform, shift-invariant wavelet transform (SIDWT), and discrete cosine harmonic wavelet transform (DCHWT) are some examples of image fusion methods based on ...
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Data preparation is the first step in data analytics projects and can include many discrete tasks such as loading data or data ingestion, data fusion, data cleaning, data augmentation, and data delivery. [2] The issues to be dealt with fall into two main categories: