[1]赵悟,段永璇,段会川,等.SIFT与Harris提取图像特征点的分析研究[J].计算机技术与发展,2018,28(12):62-66.[doi:10.3969/j.issn.1673-629X.2018.12.013]
 ZHAO Wu,DUAN Yongxuan,DUAN Huichuan,et al.Analysis and Research on Image Feature Points Extraction by SIFT and Harris[J].,2018,28(12):62-66.[doi:10.3969/j.issn.1673-629X.2018.12.013]
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SIFT与Harris提取图像特征点的分析研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年12期
页码:
62-66
栏目:
智能、算法、系统工程
出版日期:
2018-12-10

文章信息/Info

Title:
Analysis and Research on Image Feature Points Extraction by SIFT and Harris
文章编号:
1673-629X(2018)12-0062-05
作者:
赵悟1段永璇1段会川2肖宪翠2张睿1岳媛1孙小飞1范军1
1.山东省医药卫生科技信息研究所,山东 济南 250062; 2.山东师范大学 信息科学与工程学院,山东 济南 250014
Author(s):
ZHAO Wu1DUAN Yong-xuan1DUAN Hui-chuan2XIAO Xian-cui2ZHANG Rui1YUE Yuan1SUN Xiao-fei1FAN Jun1
1.Shandong Institute of Medicine and Health Information,Jinan 250062,China; 2.School of Information Science and Engineering,Shandong Normal University,Jinan 250014,China
关键词:
SIFT算法Harris角点检测算法特征点有效性计算时效性特征点相似不变性
Keywords:
SIFT algorithmHarris corner detection algorithmfeature points availabilitycomputational efficiencyfeature point invari- ance
分类号:
TP391
DOI:
10.3969/j.issn.1673-629X.2018.12.013
摘要:
SIFT(尺度不变特征变换)算法与Harris角点检测算法作为两种经典的图像特征点提取算法,在不同的图像处理中,两者体现出的图像特征点提取性能也不同。因此,如何选取合适的评价指标使两种算法在不同类型图像下提取特征点更高效,将对后续的研究与图像分析工作有重要意义。文中利用常用的折线特征主导的图像与光滑曲线特征主导的图像进行实验,并提出了一种指标评价法,即从特征点有效性、计算时效性、特征点相似不变性三方面,分别对SIFT和Harris算法在提取特征点上的有效性进行定量分析。实验结果表明。在保证特征点有效性、计算时效性以及特征点相似不变性一致的情况下,在折线特征主导的图像处理中,Harris角点检测算法与SIFT算法相比,图像特征点的提取更高效;而在光滑曲线特征主导的图像处理中,由于无法检测到足够数量的特征点,Harris角点检测算法与SIFT算法相比,图像特征点的提取性能相对较低。
Abstract:
Scale invariant feature transform (SIFT) and Harris corner detection algorithm are two classic image feature point extraction algorithms. In different image processing,they have their own traits in image feature point extraction. Therefore,how to select appropriate evaluation indexes to make the two algorithms more efficient in extracting feature points under different types of images,will be of great significance to the subsequent research and image analysis. In this paper,we take the commonly used polyline features dominant image and smooth curve features dominant image for experiment,and put forward a kind of index evaluation method. From the feature points availability,computational efficiency and feature points invariance,the effectiveness of SIFT and Harris algorithms in extracting feature points is analyzed quantitatively. The experiment shows that in the same way as the above three significant aspects,for polyline feature dominant images,Harris corner detection algorithm performs better than SIFT. But for smooth curved line feature dominant images,Harris corner detection algorithm is drastically degraded since too few feature points are detected.

相似文献/References:

[1]马强,项昭保,黄良学,等. 基于改进SIFT和RANSAC图像拼接算法研究[J].计算机技术与发展,2016,26(04):61.
 MA Qiang,XIANG Zhao-bao,HUANG Liang-xue,et al. Research on Panorama Image Mosaic Algorithm Based on Improved SIFT and RANSAC[J].,2016,26(12):61.

更新日期/Last Update: 2018-12-10