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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 ]
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]
Feature level fusion is believed to be more effective than the other levels of fusion because the feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. Therefore, fusion at the feature level is expected to provide better recognition results. [24]
Data augmentation in data analysis are techniques used to increase the amount of data. It helps reduce overfitting when training a learning algorithm. data fusion The process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. [131] data integration
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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 ...