基于改进SIFT特征提取的车标识别

耿庆田; 赵浩宇; 王宇婷

doi:10.3788/OPE.20182605.1267

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基于改进SIFT特征提取的车标识别

信息科学 | 更新时间：2020-08-13

- 基于改进SIFT特征提取的车标识别
- A vehicle logo recognition algorithm based on the improved SIFT feature
- 光学精密工程 2018年26卷第5期页码：1267-1274
- 作者机构：
  
  1.长春师范大学计算机科学与技术学院, 吉林长春 130032
  2.吉林大学计算机科学与技术学院, 吉林长春 130012
  3.吉林大学学报编辑部, 吉林长春 130012
- 作者简介：
  
  [ "耿庆田(1972-), 男, 江苏邳州人, 副教授, 2005年于吉林大学获得硕士学位, 主要从事计算机网络与智能信息系统方面的研究。E-mail:qtgeng@163.com" ]
  [ "赵浩宇(1991-), 男, 吉林长春人, 2016年于吉林大学获得硕士学位, 主要从事数字出版与智能信息系统方面的研究。E-mail:zhaohaoyu@jlu.edu.cn" ]
- 基金信息：
  
  吉林省省级产业创新专项资金资助项目(2016C078);吉林省产业技术研究和开发专项资助项目(2017C031-2);吉林省教育厅“十三五”科学技术研究资助项目(2018269)
- DOI：10.3788/OPE.20182605.1267
  中图分类号： TP391.4
- 收稿日期：2017-12-27，
  
  录用日期：2018-1-24，
  
  纸质出版日期：2018-05-25
- 稿件说明：
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耿庆田, 赵浩宇, 王宇婷. 基于改进SIFT特征提取的车标识别[J]. 光学精密工程, 2018,26(5):1267-1274.

Qing-tian GENG, Hao-yu ZHAO, Yu-ting WANG. A vehicle logo recognition algorithm based on the improved SIFT feature[J]. Optics and precision engineering, 2018, 26(5): 1267-1274.
耿庆田, 赵浩宇, 王宇婷. 基于改进SIFT特征提取的车标识别[J]. 光学精密工程, 2018,26(5):1267-1274. DOI： 10.3788/OPE.20182605.1267.

Qing-tian GENG, Hao-yu ZHAO, Yu-ting WANG. A vehicle logo recognition algorithm based on the improved SIFT feature[J]. Optics and precision engineering, 2018, 26(5): 1267-1274. DOI： 10.3788/OPE.20182605.1267.

摘要

为减少SIFT车标识别算法中检测极值点的冗余以及各种图像变化因素的不利影响，提出了基于边缘约束和全局结构化的改进SIFT算法。利用图像不变矩理论及图像边缘检测算法只对目标图像的边缘区域检测，剔除与车标识别区域无关的极值点；同时将特征点邻域划分为圆形并计算出同心圆内像素点最大曲率来构建全局SIFT组合特征向量，使SIFT描述子具有全局描述特性；并结合SVM模型作为车标图像特征向量的分类器进行特征分类、识别。仿真实验结果表明：改进的SIFT算法可以减少冗余极值点约25%~45%，提高了检测极值点的有效性；使车标平均识别率达到97%以上，改善了识别实时性。改进SIFT的车标识别方法与几种常用的图像特征提取算子相比较具有识别率高、识别速度快的优点。

Abstract

In order to reduce redundancy of detecting extreme point and various adverse effects of image change factors during using SIFT vehicle logo recognition algorithms. An improved SIFT algorithm based on edge constraint and global structure was proposed

it took advantages of the image moment invariant theory and the image edge detection algorithm to only detect edge regions of target image

eliminating extreme points that have nothing to do with vehicle logo recognition regions

and it divided each feature point neighborhood into circular regions and calculated the maximum curvature of pixel in each group of concentric circles that obtained by the division to construct the global SIFT combination feature vectors

which made the SIFT descriptors had a global describing nature. It also combined the SVM model such that a feature vector classifier of vehicle logo image was created to classify features and recognize vehicle logos. The simulation experiment data indicates that the improved SIFT vehicle logo recognition algorithm may reduce redundant extreme points by about 25 to 45 percent

which enhances the effectiveness of detecting extreme points

and make the average recognition rate reach more than 97 percent

which improves the real-time trait of recognition. It can be seen that higher recognition rate and faster recognition speed can be obained in comparison with several common image feature extraction operators.

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references

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