Rychtáriková Renata, Korbel Jan, Macháček Petr, Štys Dalibor
Institute of Complex Systems, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Kompetenzzentrum MechanoBiologie in Regenerativer Medizin, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136, 373 33 Nové Hrady, Czech Republic.
Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
Entropy (Basel). 2018 Feb 3;20(2):106. doi: 10.3390/e20020106.
We introduce novel information-entropic variables-a Point Divergence Gain ( Ω α ( l → m ) ), a Point Divergence Gain Entropy ( I α ), and a Point Divergence Gain Entropy Density ( P α )-which are derived from the Rényi entropy and describe spatio-temporal changes between two consecutive discrete multidimensional distributions. The behavior of Ω α ( l → m ) is simulated for typical distributions and, together with I α and P α , applied in analysis and characterization of series of multidimensional datasets of computer-based and real images.
我们引入了新的信息熵变量——点散度增益（Ωα(l→m)）、点散度增益熵（Iα）和点散度增益熵密度（Pα），它们源自雷尼熵，用于描述两个连续离散多维分布之间的时空变化。针对典型分布模拟了Ωα(l→m)的行为，并将其与Iα和Pα一起应用于基于计算机的图像和真实图像的多维数据集系列的分析与表征。
