基于形态学和细胞神经网络的边缘检测方法

Edge Detection Based on Morphology and Cellular Neural Network

采用形态学方法提取二值图像的边缘,利用二值形态学与离散细胞神经网络(DT-CNN)的某种天然对应关系,将离散细胞神经网络引入形态学,将形态学运算转化为某种特定模板下的多层离散细胞神经网络,然后对该模板进行优化设计使之转化为单层离散细胞神经网络,降低了运算的复杂度。在此基础上,通过综合灰度图像像素在每个比特位上的边缘检测结果,提出了采用二值形态学提取灰度图像边缘的方法。与传统边缘提取方法Sobel和Log相比较,该方法边缘提取效果良好,收敛迅速。

Morphology is used for edge extraction of binary image. In particular, Discrete-time Cellular Neural Network （DT-CNN） is introduced into binary morphology owing to the natural corresponding relationship between them, and then the morphology operation is transformed into multi-layer DT-CNN under some specific template. Multi-layer DT-CNN is transformed into Monolayer DT-CNN by optimizing design to reduce the complexity of the operation. Therefore, we can extract the edge of gray image by synthesizing the edge detection result of each bit in the pixel. Compared with the traditional Sobel and Log operator, the method has a better performance and converges quickly.