用简化脉冲耦合神经网络实现交通标志图像的类Euclidean距离变换类内特征提取

王蒙军; 阳路; 王霞; 刘剑飞

doi:10.3788/OPE.20122012.2751

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用简化脉冲耦合神经网络实现交通标志图像的类Euclidean距离变换类内特征提取

信息科学 | 更新时间：2020-08-13

- 用简化脉冲耦合神经网络实现交通标志图像的类Euclidean距离变换类内特征提取
- Realization of within-class feature extracting based on Euclidean-like distance transform for traffic signs using simplied PCNN
- 光学精密工程 2012年20卷第12期页码：2751-2758
- 作者机构：
  
  1. 河北工业大学信息工程学院天津,300401
  2. 河北工业大学-Xilinx信号检测与处理联合实验室天津,300401
- 作者简介：
- 基金信息：
  
  河北省高等学校科学技术研究青年基金资助项目(No.2010121)()
- DOI：10.3788/OPE.20122012.2751
  中图分类号： TP391.41
- 收稿日期：2012-07-19，
  
  修回日期：2012-10-12，
  
  纸质出版日期：2012-12-10
- 稿件说明：
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王蒙军, 阳路, 王霞, 刘剑飞. 用简化脉冲耦合神经网络实现交通标志图像的类Euclidean距离变换类内特征提取[J]. 光学精密工程, 2012,20(12): 2751-2758

WANG Meng-jun, YANG Lu, WANG Xia, LIU Jian-fei. Realization of within-class feature extracting based on Euclidean-like distance transform for traffic signs using simplied PCNN[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2751-2758
王蒙军, 阳路, 王霞, 刘剑飞. 用简化脉冲耦合神经网络实现交通标志图像的类Euclidean距离变换类内特征提取[J]. 光学精密工程, 2012,20(12): 2751-2758 DOI： 10.3788/OPE.20122012.2751.

WANG Meng-jun, YANG Lu, WANG Xia, LIU Jian-fei. Realization of within-class feature extracting based on Euclidean-like distance transform for traffic signs using simplied PCNN[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2751-2758 DOI： 10.3788/OPE.20122012.2751.

摘要

脉冲耦合神经网络(PCNN)提取的特征序列的旋转不变性降低了道路交通标志类内匹配识别的准确性

为了提取更有利于形状分类的特征向量

本文利用PCNN的自动波扩散特性

简化了PCNN模型。采用简化PCNN模型产生的类Euclidean距离图像作为分类特征

利用最小方差值进行匹配分析

并通过实验选取了最佳PCNN参数。针对道路交通标志图像库GB5768-1999的实验结果表明

采用获得的类Euclidean距离图像作为特征向量进行分类匹配

在选定边缘图像的迭代次数

为16

反馈输入固有电势

为0.65

动态门限固有电势

为100

卷积核矩阵为55时

最小方差值均出现在对应的标准图像位置。结果表明

简化PCNN的类Euclidean距离变换能够有效提取二值边缘图像的形状信息。该方法优于传统PCNN熵序列的特征向量方法

类内区分效果更加明显。

Abstract

The recognition accuracy of with-in class matching traffic signs is dropped because of the rotation invariance in feature sequences extracted by conventional Pulse Coupled Neural Network(PCNN). In order to get a new feature vector with stronger classification ability

the PCNN mode was simplified according to its automatic wave diffusion characteristics. An Euclidean-like distance image was used as the new feature vector

and a match analysis was carried out by the minimum variance. Then

optimal parameters of PCNN were acquired through experiments. The experimental results based on GB5768-1999 traffic signs show that when the Euclidean-like distance transform based on simplified PCNN is used as the feature vector for classification matching

the variance of test image corresponding to standard images can achieve the minimum value in acquiring edge image with the number iterations of 16

magnitude adjustments of feeding input of 0.65

magnitude adjustments of dynamic threshold of 100

and convolution kernel matrix in 5 5. It concludes that the Euclidean-like distance transform based on simplified PCNN can expand the shape information of edge images effectively. It is superior to the feature vector based on entropy sequence

and is fit for the identification of target images in with-in classes.

关键词

Keywords

references

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