Abstract
The subject of this chapter is semantic equalization. This is the conversion of input data which does not refer to the same object or phenomena to a common object or phenomena. Different inputs can only be fused together if they refer to the same object or phenomena. In the case of image fusion we normally assume this to be the case if the images are captured by the same or similar type of camera. However, in the case of featuremap fusion, the featuremaps rarely refer to the same object or phenomena. In this case, fusion can only take place if the features maps are semantically equivalent. This is also true in the case of decision map fusion. In this chapter we shall therefore concentrate on the semantic equivalence of feature maps and decision maps.
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Mitchell, H.B. (2010). Semantic Equivalence. In: Image Fusion. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11216-4_5
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DOI: https://doi.org/10.1007/978-3-642-11216-4_5
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