Cell Neural Network Research Articles

Edge Detection for Color Image Based on CNN

According to cell neural network (CNN) model, this paper presents a new color image edge detection algorithm which inherits the advantage of CNN and makes up the disadvantage of CNN method which can not directly deal with color images. This algorithm takes full advantage of color image information, through euclidean distance measures the difference between pixels, so that CNN equation can be carried out to compute in the RGB color space. According to theoretical analysis and robustness studies for CNN template, this paper also presents a CNN robustness theorem which can implement edge detection for color image. This theorem provided analytical criterion for designing parameters of the corresponding CNN templates. The experimental results show that the proposed approach can effectively carry out edge extraction and quantitative assessments verify the advantage of CNN edge detection which can locates accurately.

An Improved Approach of Color Edge Detection Based on Robustness of Cell Neural Network

For the disadvantage of cell neural network (CNN) method which can not directly deal with color images, we present a new color image edge detection algorithm according to CNN model. Through robustness analysis for CNN template, a CNN theorem be carried out which can compute in the RGB color space. The experimental results show that our approach can effectively carry out edge extraction and locates accurately.

A Novel Approach for Vehicle License Plate Locating Based on CNN Color Image Edge Detection

摘要: 针对现有车牌定位算法准确率不高、步骤多和速度慢等问题, 提出一种彩色图像车牌定位方法(License plate locating based on CNN color edge detection, LPLCCED). 首先利用细胞神经网络(Cell neural network, CNN)模型导出一种与车牌颜色特征相结合的车牌定位专用边缘检测算法, 将车牌的颜色对约束条件融合到边缘检测算法中, 本文专用边缘检测算法可以大大缩小车牌初步定位的范围. 接下来提出一种针对车牌特征的边缘滤波算法, 最后根据车牌结构和纹理特征对候选区域进行判别验证. 该流程的各个环节都可以通过硬件实现, 为面向智能交通领域的实时车牌识别系统的前期车牌定位处理提供了依据. 关键词: 车牌定位 / 彩色边缘检测 / 细胞神经网络 / 边缘滤波 / 人类视觉系统