摘要
为提高信息熵的计算速度及提升基于信息熵的信息处理效率,提出了一种反积型信息熵。首先,给出其定义,分析并证明其相关性质;然后,从数学理论上证明了其与香农熵的一致性;最后,将反积型信息熵在基于二维阈值最大熵值图像分割算法中进行了应用。结果表明:与基于香农熵的二维阈值分割效果相比,基于反积型信息熵的二维阈值分割效果更佳,可有效节约计算时间,且具有很强的实用性。
In order to improve the computation speed of information entropy and improve the efficiency of information processing based on information entropy,a kind of reverse product information entropy is presented. Firstly,the reverse product information entropy is definited,and its properties are proved.Then the consistency between the reverse product information entropy and Shannon entropy is proved mathematically. Finally,the reverse product information entropy is applied to the image segmentation algorithm based on two-dimensional threshold maximum entropy. The experimental results show that the two-dimensional threshold segmentation effect based on the reverse product information entropy is better than that of two-dimensional threshold segmentation based on Shannon entropy,and it can save computing time effectively,and it is very practical.
作者
王帅
李婷
于永利
WANG Shuai;LI Ting;YU Yong-li(Information Engineering Department, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China;Equipment Command and Management Department , Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China)
出处
《装甲兵工程学院学报》
2018年第1期113-118,共6页
Journal of Academy of Armored Force Engineering
关键词
信息熵
反积型信息熵
图像分割
二维阈值分割
information entropy
reverse product information entropy
image segmentation
two-dimensional threshold segmentation
