基于细胞神经网络边缘检测的自适应研究

Adaptive research based on the edge detection of cellular neural networks

针对细胞神经网络(CNN)在边缘提取中简单自适应模板对灰度图像边缘不完整的问题,提出了对简单模板进行改进,将四分模板细化为六分模板的方法.首先均分简单模板中的判定范围,然后重新推导在不同范围中各个自适应模板的各项取值,再对图像处理中常用的灰度图像进行边缘提取并与简单自适应模板的结果进行比较.实验结果表明:改进的自适应模板相比较于简单的自适应模板可以提取出更完整、清晰的边缘.

In order to solve the problem of incomplete edge of gray image by simple adaptive template in edge extraction by cellular neural network(CNN), a method is proposed to improve the simple template and refine the four-part template into six-part template. Firstly, the judgment range of the simple template is divided evenly.Secondly, the values of each adaptive template in different ranges are derived again. Finally, the edge extraction of the gray image commonly used in image processing is carried out and the results of the simple adaptive template are compared. Experimental results show that the improved adaptive template can extract more complete and clear edges than the simple adaptive template.