Fusion Techniques for Reliable Information: A Survey <s> Multiple Metrics (MM) <s> Fusionplex is a system for integrating multiple heterogeneous and autonomous information sources that uses data fusion to resolve factual inconsistencies among the individual sources. To accomplish this, the system relies on source features, which are meta-data on the merits of each information source; for example, the recentness of the data, its accuracy, its availability, or its cost. The fusion process is controlled with several parameters: (1) with a vector of feature weights, each user defines an individual notion of data utility; (2) with thresholds of acceptance, users ensure minimal performance of their data, excluding from the fusion process data that are too old, too costly, or lacking in authority, or numeric data that are too high, too low, or obvious outliers; and, ultimately, (3) in naming a particular fusion function to be used for each attribute (for example, average, maximum, or simply any) users implement their own interpretation of fusion. Several simple extensions to SQL are all that is needed to allow users to state these resolution parameters, thus ensuring that the system is easy to use. Altogether, Fusionplex provides its users with powerful and flexible, yet simple, control over the fusion process. In addition, Fusionplex supports other critical integration requirements, such as information source heterogeneity, dynamic evolution of the information environment, quick ad-hoc integration, and intermittent source availability. The methods described in this paper were implemented in a prototype system that provides complete Web-based integration services for remote clients. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Multiple Metrics (MM) <s> Image fusion is the process of combining information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing. The objective in image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information particular to an application or task. Given the same set of input images, different fused images may be created depending on the specific application and what is considered relevant information. There are several benefits in using image fusion: wider spatial and temporal coverage, decreased uncertainty, improved reliability, and increased robustness of system performance. Often a single sensor cannot produce a complete representation of a scene. Visible images provide spectral and spatial details, and if a target has the same color and spatial characteristics as its background, it cannot be distinguished from the background. If visible images are fused with thermal images, a target that is warmer or colder than its background can be easily identified, even when its color and spatial details are similar to those of its background. Fused images can provide information that sometimes cannot be observed in the individual input images. Successful image fusion significantly reduces the amount of data to be viewed or processed without significantly reducing the amount of relevant information. <s> BIB002
We can apply multiple metrics into image fusion as a fusion process method. MM is suited for cases where the feature values are continuous variables  . Fusion at higher levels designed for situation awareness can be expected to encounter a spectrum of the different types of features, which include integer valued and symbolic valued data, which require more complex metrics BIB001 . The main objective of image fusions is to minimize the uncertainty and the redundancy while maximizing the relevant information particular to applications BIB002 .