[1]邱丽君[],唐加山[].一种快速的两步骤图像匹配新算法[J].计算机技术与发展,2015,25(08):67-70.
　QIU Li-jun[],TANG Jia-shan[]. A New Fast Two-step Image Matching Algorithm[J].,2015,25(08):67-70.
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一种快速的两步骤图像匹配新算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年08期
页码:
67-70
栏目:
智能、算法、系统工程
出版日期:
2015-08-10

文章信息/Info

Title:
 A New Fast Two-step Image Matching Algorithm
文章编号:
1673-629X（2015)08-0067-04
作者:
 邱丽君[1];  唐加山[2]
 1.南京邮电大学 通信与信息工程学院；2.南京邮电大学 理学院,
Author(s):
 QIU Li-jun[1] ; TANG Jia-shan[2]
关键词:
 ; 由粗到细; 快速图像匹配; 双直方图哈希算法; ORB算子
Keywords:
 ; coarse-to-fine; fast image matching; two-column histogram hashing algorithm; ORB operator
分类号:
TP391.41
文献标志码:
A
摘要:
 图像匹配时间包括特征提取时间和特征点匹配时间，减少特征提取时间，能够大大提高图像匹配效率。目前，普遍的匹配算法对整幅图像进行特征提取，当图像较大时，特征提取时间长，影响匹配效率。文中提出一种由粗到细的两步骤快速图像匹配新算法，这种算法在特征提取时间上作了改进。粗匹配阶段，用双直方图( TCH)哈希算法进行模板匹配，找到与模板最相似的图像区域，缩小ORB特征提取的范围。细匹配阶段，在找到的最相似区域，用高速的ORB算子提取和描述特征点。最后，用欧氏距离法进行特征向量的匹配。由于特征提取的范围被缩小到一个很小的区域，总匹配时间大大减少。实验结果表明，文中提出的图像匹配算法，在保持高匹配鲁棒性的前提下，与SIFT、SURF和ORB算法相比，匹配速度有了很大提高。
Abstract:
 Image matching time includes feature extraction time and feature points matching time, reduction of image matching time can enormously enhance the efficiency of image matching. Presently,common matching algorithm extract features in the whole picture. Time of feature extraction can be very long when the image be processed is big,which depresses matching efficiency. A fast image matching method using a novel two-step searching strategy ( coarse-to-fine) is proposed in this paper. At coarse matching stage,a novel two-col-umn histogram hashing is used to find approximate location where target object may appear,narrowing the scope of the ORB feature ex-traction. In the refining stage,key points are detected and described in the coarser scales using ORB. The Euclidean distance strategy is then employed to implement matching. Narrowing the feature extraction scale to a small area,so the whole image matching time is de-creased largely. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in the speed of image matching on the premise of maintaining the high matching robustness.

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更新日期/Last Update: 2015-09-11