二值图像逻辑或运算CNN模板的鲁棒性设计

Robustness Design of Binary Image Logic or CNN Template

当前对二值图像进行逻辑或运算的算法较少,本文对一种能实现二值图像逻辑或运算的细胞神经网络(cellular neural networks)模板进行研究,提出了一种新算法。通过设定二值图像逻辑或运算法则,并分析LOGOR CNN模板的鲁棒性,提出了一个定理,最后进行了严格的数学证明。只要实际使用的模板参数符合定理中给出的参数范围,CNN就能实现对二值图像的逻辑或运算。实验仿真证明了LOGOR CNN在实际应用中的有用性及鲁棒性设计定理的正确性。

Recently operations on binary image by using the Boolean or algorithm is less,in this paper,we study a kind of Cellular Neural Networks(Cellular Neural Networks) template that can realize logic or of binary image,proposed a new algorithm.By setting binary image logic or algorithms,and analyzes LOGOR CNN template robustness,proposed a theorem,finally gives a strict mathematical proof. As long as the actual use of the template parameters conform to theorem is given in the parameter range,CNN can realize logic or operation of binary image.Experimental results verify the effectiveness of LOGOR CNN and the feasibility of the design robustness theorem in practical application.