一种改进的图像边缘检测方法——Weighted CHNN  

A Improved Image Edge Detection Method-Weighted CHNN

作　　者：刘双强[1] 

机构地区：[1]湖南工业大学科技学院,湖南株洲412008

出　　处：《微电子学与计算机》2011年第9期100-103,107,共5页Microelectronics & Computer

摘　　要：文中提出了一种改进的CHNN方法,称为Weighted CHNN(加权的CHNN,简称WCHNN)方法.该方法在CHNN神经网络元的n个连接上施加权值,可以通过各种局部搜索、优化算法,使用指定的样本输入、样本输出等方法来训练该WCHNN网络从而确定各权值,使得WCHNN在保留了CHNN的优点的同时,还可以根据不同的样本输入输出图像来调节边缘检测的灵敏度,从而提高检测结果质量并避免检测结果中出现边缘过宽的情况.实验结果表明,训练后的WCHNN网络,比起CHNN有着更低的边缘检测错误率,并可检出原来CHNN方法漏检的边缘.An improved CHNN method which is called Weighted CHNN（WCHNN） is constructed.This method puts n weights on the n connections of each sub-network of CHNN.They can be determined by training the WCHNN network using local search and optimization methods with the designated sample input and output images.Thus WCHNN gains the abilities of tuning the sensitivity of edge detection according to different sample images and correcting the too-wide edges detected by CHNN,while retaining the advantages of CHNN.Experimental results show that the trained WCHNN network can get lower error rate while processing the images which noise rate is similar to sample input images than CHNN,and it can also detect those methods missed by CHNN.

关 键 词：图像边缘检测 CHNN 人工神经网络 加权参数 参数训练 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]