Fusion Techniques for Reliable Information: A Survey <s> Figure 2. Fusion Types based on the Definition <s> 1 Introduction: The Role of Data Fusion in Sensory Systems.- 2 Bayesian Sensory Information Processing.- 3 Information Processing Using Energy Function Minimization.- 4 Weakly vs. Strongly Coupled Data Fusion: A Classification of Fusional Methods.- 5 Data Fusion Applied to Feature Based Stereo Algorithms.- 6 Fusing Binocular and Monocular Depth Cues.- 7 Data Fusion in Shape from Shading Algorithms.- 8 Temporal Aspects of Data Fusion.- 9 Towards a Constraint Based Theory of Sensory Data Fusion. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Figure 2. Fusion Types based on the Definition <s> Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion. <s> BIB002 </s> Fusion Techniques for Reliable Information: A Survey <s> Figure 2. Fusion Types based on the Definition <s> Data Fusion is a very broad interdisciplinary technology domain. It provides techniques and methods for; integrating information from multiple sources and using the complementarities of these detections to derive maximum information about the phenomenon being observed; analyzing and deriving the meaning of these observations and predicting possible consequences of the observed state of the environment; selecting the best course of action; and controlling the actions. Here, the focus is on the more mature phase of data fusion, namely the detection and identification/classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications. It is necessary to: expand on the data fusion methodology pertinent to Situation Monitoring, Incident Detection, Alert and Response Management; discuss some related Cognitive Engineering and visualization issues; provide an insight into the architectures and methodologies for building a data fusion system; discuss fusion approaches to image exploitation with emphasis on security applications; discuss novel distributed tracking approaches as a necessary step of situation monitoring and incident detection; and provide examples of real situations, in which data fusion can enhance incident detection, prevention and response capability. In order to give a logical presentation of the data fusion material, first the general concepts are highlighted (Fusion Methodology, Human Computer Interactions and Systems and Architectures), closing with several applications (Data Fusion for Imagery, Tracking and Sensor Fusion and Applications and Opportunities for Fusion). <s> BIB003
The two concepts are similar to each other. However, the definition of "fusion" has a different meaning by depending on whether a machine or a user processes the events. As described in Figure. 2, "Machine fusion" is the integration by computers for obtaining data from multiple sources. This fusion is utilized to get a unified, a completed, and an accurate representation for objects BIB001 . For instance, the fusion of a signal processing of sensor measurements, which is used for identification, gets information from objects. Among machine fusion techniques, "Data fusion" considers a dynamic process such as the raw data association or correlation for fused product  . This fusion improves the accuracy than any product which utilizes separate raw data elements. "Sensor fusion" aggregates data from multi-sensors to provide an improved accuracy when it compared with only a single sensor value BIB002 . This fusion achieves a reliable inference by predicting the entity states and by reducing uncertainty. "Information fusion" is a multilevel process. This fusion supports data mining and the integration of information from selected multi-sources to produce the fused information BIB003 . It has a greater value than any of its parts. "Cognitive fusion" is one of user processes for integrating and combining contexts to generate knowledge about the situation. This fusion helps to make a decision for the situation awareness in pervasive computing applications  .