Vehicle Logo Recognition Based on Local Feature Descriptor

被引：2
|
作者：
Wang, Shengke [1 ]
Liu, Lili [1 ]
Xu, Xiaowei [1 ]
机构：
[1] Ocean Univ China, Dept Comp Sci & Technol, Qingdao, Shandong, Peoples R China
来源：
INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY, PTS 1-4 | 2013年 / 263-266卷
关键词：
vehicle; feature-matching; SIFT; SURF; query image; train images;
D O I：
10.4028/www.scientific.net/AMM.263-266.2418
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we present a comparison of the scale-invariant feature transforms (SIFT)-based feature-matching scheme and the speeded up robust features (SURF)-based feature-matching scheme in the field of vehicle logo recognition. We capture a set of logo images which are varied in illumination, blur, scale, and rotation. Six kinds of vehicle logo training set are formed using 25 images in average and the rest images are used to form the testing set. The Logo Recognition system that we programmed indicates a high recognition rate of the same kind of query images through adjusting different parameters.
引用
收藏
页码：2418 / +
页数：2
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