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Internal Fusion Functions
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2018-04-01 , DOI: 10.1109/tfuzz.2017.2686345
Daniel Paternain , Maria Jesus Campion , Radko Mesiar , Irina Perfilieva , Humberto Bustince

In this paper, we investigate a mechanism for fusing a set of inputs (values) in such a way that the procedure does not create new information during the process. In order to do so, we introduce internal fusion functions, a family of fusion functions in which the output always corresponds to some of the given inputs. We perform an in-depth theoretical study of internal fusion functions and, furthermore, we propose three different construction methods, which are based on 1) an arbitrary fusion function and a partition of the domain; 2) a linear order; and 3) a minimization mechanism using penalty functions. Finally, we illustrate this paper with the application of internal fusion functions in two image processing algorithms where a set of images must be fused, namely multifocus image and denoised image fusion, as well as in an example of multiclass problem, where we fuse a set of score matrices obtained by several classification algorithms.

中文翻译:

内部融合函数

在本文中,我们研究了一种融合一组输入(值)的机制,该机制在此过程中不会创建新信息。为此,我们引入了内部融合函数,这是一组融合函数,其中输出始终对应于某些给定的输入。我们对内部融合函数进行了深入的理论研究,此外,我们提出了三种不同的构造方法,它们基于:1)任意融合函数和域的划分;2) 线性顺序;3) 使用惩罚函数的最小化机制。最后,我们通过内部融合函数在必须融合一组图像的两种图像处理算法中的应用来说明本文,即多焦点图像和去噪图像融合,
更新日期:2018-04-01
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