基于细胞神经网络鲁棒性的彩色图像边缘检测

根据细胞神经网络(CNN)数学模型，提出一种新的彩色图像边缘检测方法。 新方法继承了CNN 的优点，解决了CNN 现有算法不能直接检测彩色图像边缘的问题。该 方法充分利用图像中的颜色信息，通过欧几里得距离度量像素之间的差异，使CNN 方程可 以在RGB 彩色空间中进行运算。对CNN 模板进行理论分析和鲁棒性研究，提出一个实现 彩色图像边缘检测功能要求的CNN 鲁棒性定理，为设计相应的CNN 模板参数提供了解析 判据。实验结果表明，该方法可以对彩色图像进行有效的边缘提取，定量评价验证了CNN 边缘检测定位准确的优点。

A novel color edge detection based on robustness of cell neural network

Based on the cellular neutral network(CNN) mathematical model, a new method of color image edge detection is proposed. It inherits the advantages of CNN, and fills up a gap that the existing algorithms cannot directly detect the color image edge. This method takes advantage of the color information in the image to measure the differences between pixel by the Euclidean distance, allowing the CNN equation for operation in RGB color space. To make a theoretical analysis and robustness research for CNN template, a CNN robustness theorem is proposed that achieves the functional requirements of the color image edge detection, and provides the analytical criteria for designing the corresponding CNN templates parameters. The experimental results show that this algorithm is effective for the color image edge extraction. A quantitative evaluation validates the advantages of the accurate edge detection positioning.