PCA-SC Shape Matching for Object Recognition

HUANG Wei-guo; GU Chao; ZHU Zhong-kui

doi:10.3788/OPE.20132108.2103

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PCA-SC Shape Matching for Object Recognition

更新时间：2020-08-12

- PCA-SC Shape Matching for Object Recognition
- Optics and Precision Engineering Vol. 21, Issue 8, Pages: 2103-2110(2013)
- 作者机构：
  
  苏州大学城市轨道交通学院,江苏苏州,215131
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132108.2103
  CLC： TP391.4
- Received：11 March 2013，
  
  Revised：10 April 2013，
  
  Published Online：20 August 2013，
  
  Published：15 August 2013
- 稿件说明：
移动端阅览
黄伟国顾超朱忠奎. 用于目标识别的PCA-SC形状匹配算法[J]. 光学精密工程, 2013,21(8): 2103-2110

HUANG Wei-guo GU Chao ZHU Zhong-kui. PCA-SC Shape Matching for Object Recognition[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2103-2110
黄伟国顾超朱忠奎. 用于目标识别的PCA-SC形状匹配算法[J]. 光学精密工程, 2013,21(8): 2103-2110 DOI： 10.3788/OPE.20132108.2103.

HUANG Wei-guo GU Chao ZHU Zhong-kui. PCA-SC Shape Matching for Object Recognition[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2103-2110 DOI： 10.3788/OPE.20132108.2103.

摘要

基于形状上下文（Shape Context）算法并融合主成分分析（PCA）的降维思想，提出了一种PCA-SC算法来提高形状匹配和目标识别的速度和抗噪能力。该算法将SC算法获取的特征矩阵构成协方差矩阵，按照特征值由大到小的准则进行降维，形成新的特征矩阵用于匹配和识别，既抑制了噪声干扰，提高了识别准确率，又能够提高匹配速度，易于满足工程应用对实时性的要求。利用MNIST图像数据库中的图像进行了实验分析，结果表明，PCA-SC算法在保持了SC算法原有的定位准确、抑制噪声等优点的基础上，识别速度提高了1倍；准确率达到了96.15%，提高了约0.5%；而且抗噪性更强，可用于匹配和识别较复杂的形状和目标。该算法基本满足匹配和识别对速度、准确率和抗干扰性等方面的要求。

Abstract

A new algorithm based on Shape Context(SC) and Principal Component Analysis(PCA）called PCA-SC was proposed to improve the matching efficiency and anti-noise performance in shape matching and object recognition. The algorithm establishes a covariance matrix based on the feature matrix obtained by the SC

then reduces its dimensions according to the size of eigen value and forms a new feature matrix to implement the shape matching and object recognition. The proposed algorithm can not only remove noise interference and improve the recognition accuracy

but also can enhance the matching efficiency for real-time application. The experimental results of MNIST database indicate that the PCA-SC algorithm outperforms previous SC algorithm

and its recognition speed is doubled that of SC and the accuracy reaches to 96.15% increased by 0.5%. Furthermore

the anti-noise performance becomes stronger. Therefore

this novel algorithm shows better performance for shape matching and object recognition in efficiency

accuracy and anti-noise.

关键词

Keywords

references

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