基于四元数和SOM神经网络的彩色图像边缘检测

doi:10.3724/SP.J.1087.2012.00510

计算机应用 ›› 2012, Vol. 32 ›› Issue (02): 510-513.DOI: 10.3724/SP.J.1087.2012.00510

基于四元数和SOM神经网络的彩色图像边缘检测

王铮,李兴民

华南师范大学计算机学院，广州 510631

收稿日期:2011-08-16 修回日期:2011-09-30 发布日期:2012-02-23 出版日期:2012-02-01
通讯作者: 王铮
作者简介:王铮(1986-)，女，河南濮阳人，硕士研究生，主要研究方向：图形图像处理；
李兴民(1957-)，男，山东陵县人，教授，博士，主要研究方向：小波分析、图像处理。
基金资助:
广东省和中科院产学研合作项目(2009B080701077)

Color image edge detection based on quaternion and self-organizing map neural network

WANG Zheng,LI Xing-ming

School of Computer Science, South China Normal University, Guangzhou Guangdong 510631, China

Received:2011-08-16 Revised:2011-09-30 Online:2012-02-23 Published:2012-02-01
Contact: WANG Zheng

摘要/Abstract

摘要： 针对传统边缘检测方法无法考虑到彩色图像各颜色分量的相关性，以及边缘提取效果受阈值影响的不足，提出一种四元数和自组织神经网络(SOM)相结合的彩色图像边缘检测算法。根据四元数柯西积分公式和四元数矢量积性质，构造图像的边缘特征向量对SOM神经网络进行训练，然后用训练好的SOM网络提取边缘。实验表明，该方法具有良好的边缘检测效果，并有较强的细节保持能力。

关键词: 四元数, 柯西积分公式, 四元数矢量积, 特征向量, 自组织映射, 边缘检测

Abstract: As the correlation between color components in color image is not considered in the traditional edge detection algorithms, and the detection results will be affected by the threshold value, a new kind of edge detection method for color image was proposed combining quaternion with Self-Organizing Map (SOM). Based on the quaternionic Cauchy integral formula and vector product, the edge characteristic vector was constructed. Then the SOM was trained by the vector and used for edge detection. The experimental results show that the proposed method has stronger retention capacity of the details.

Key words: quaternion, Cauchy integral formula, quaternion vector product, characteristic vector, Self-Organizing Map (SOM), edge detection

中图分类号:

TP317.4

王铮李兴民. 基于四元数和SOM神经网络的彩色图像边缘检测[J]. 计算机应用, 2012, 32(02): 510-513.

WANG Zheng LI Xing-ming. Color image edge detection based on quaternion and self-organizing map neural network[J]. Journal of Computer Applications, 2012, 32(02): 510-513.

参考文献

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基于四元数和SOM神经网络的彩色图像边缘检测

Color image edge detection based on quaternion and self-organizing map neural network

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