采用多形状特征融合的多视点目标识别

李平; 魏仲慧; 何昕; 何丁龙; 何家维; 梁国龙; 凌剑勇

doi:10.3788/OPE.20142212.3368

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采用多形状特征融合的多视点目标识别

信息科学 | 更新时间：2020-08-13

- 采用多形状特征融合的多视点目标识别
- Object recognition based on shape feature fusion under multi-views
- 光学精密工程 2014年22卷第12期页码：3368-3376
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
  
  国防科技预研基金资助项目(No.1040603)()
- DOI：10.3788/OPE.20142212.3368
  中图分类号： TP391
- 收稿日期：2013-12-21，
  
  修回日期：2014-02-20，
  
  纸质出版日期：2014-12-25
- 稿件说明：
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李平, 魏仲慧, 何昕等. 采用多形状特征融合的多视点目标识别[J]. 光学精密工程, 2014,22(12): 3368-3376

LI Ping, WEI Zhong-hui, HE Xin etc. Object recognition based on shape feature fusion under multi-views[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3368-3376
李平, 魏仲慧, 何昕等. 采用多形状特征融合的多视点目标识别[J]. 光学精密工程, 2014,22(12): 3368-3376 DOI： 10.3788/OPE.20142212.3368.

LI Ping, WEI Zhong-hui, HE Xin etc. Object recognition based on shape feature fusion under multi-views[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3368-3376 DOI： 10.3788/OPE.20142212.3368.

摘要

研究了多视点下三维目标的识别问题.针对传统的采用单一特征的方法在目标描述方面的不足

提出了一种融合多种特征的识别算法.首先

利用各向异性高斯方向导数相关矩阵提取目标角点

采用骨架约束提取特征角点

将各特征角点到目标质心的归一化距离作为角点描述子.接着

分别提取目标的几何矩不变量、仿射矩不变量、目标边界的傅里叶描述子;计算4种特征的类内和类间散布矩阵;以样本散布矩阵的迹作为权重

加权融合4种特征.然后

对融合后的特征向量进行独立成分分析(ICA)

得到相互独立的特征分量.最后

采用支持向量机的分类方法进行分类.实验结果表明

本文提出的方法比采用单一特征的方法的正确识别率平均提高10%以上

且在小训练样本(10%总体样本)情况下仍能获得80%以上的识别率

可满足经纬仪实时目标识别系统的要求.

Abstract

Three dimensional(3D) object recognition was researched under multi-view points. For the shortages of traditional signal feature description for 3D object recognition under multi-view points

a new recognition algorithm fusing multiple features was proposed. Firstly

the object corners were extracted by using the correlation matrixes of anisotropic Gaussian directional derivatives

the particular corners were selected by the skeleton constraint

and the normalized distance between particular corners and the object centroid was taken as the corner descriptor. Then

the geometric moment invariants

affine moment invariants

and the Fourier descriptor of object boundary were extracted

respectively

and the scatter matrixes within and between classes for the four features were calculated. By taking the trace of sample scatter matrix as the weight

the four features were fused. Furthermore

the Independent Component Analysis (ICA) was carried out on the fused vector to obtain independent features. Finally

a Support Vector Machine (SVM) was adopted to complete the whole classification of the experiments. Experimental results show that the recognition accuracy of the proposed approach is higher than that of the signal feature approach by 10% averagely and that in the small training sample (10% of the total samples) condition still achieves more than 80%.It concludes that proposed algorithm meets the demand of theodolites for real-time object recognition.

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references

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