用细胞神经网络提取二值与灰度图象边缘

边缘是图象的重要特征，采用细胞神经网络提取图象边缘时，网络参数的选择是一个重要问题。为了能够有效地提取图象边缘，基于高通滤波模板，选择了细胞神经网络的一组简单易行的参数，首先将其用于检测二值图象边缘，再在此基础上，通过综合灰度值各位面边缘检测的结果提取出灰度图象的边缘。与传统边缘提取方法Sobel和Log方法的比较可见，该方法是有效的，并且由于细胞神经网络具有高速并行运算、便于硬件实现等特点，因此使其在图象实时处理中具有更大的潜力。

Edge Detection of Binary Images and Gray-Scale Images using Cellular Neural Networks

Edge is an important feature of images. There are many ways to detect the edge of animage. In this paper, the cellular neural network is proposed for edge detection. Cellular neural network is a large scale nonlinear analog circuit suitable for real-time signal and image processing. The key problem is to find a set of parameters for the network. The high-pass filter is utilized to design the parameters of cellular neural network for detecting the binary images. A gray-scale image can be divided into 2 binary planes with different gray level. The edge of gray-scale images then can be detected through synthesizing the edge of each binary plane. Finally, the edge detection result of CNN is compared with that of Sobel and Log algorithms It can be seen from the simulation results that the proposed method is effective. Besides, because the cellular neural networks can use high-speed parallel computation and is easy to be implemented in hardware, therefore it has more potential in real-time image processing.