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一种提高SIFT特征匹配正确率的方法

A method for improving matching accuracy of SIFT features
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摘要 针对尺度不变特征变换(SIFT)特征匹配算法存在计算量大、实时性差、误匹率高的问题,提出一种基于距离比率准则的方法来去除SIFT特征匹配中的错误匹配。传统的方法是采用随机选取一致性(RANSAC)方法选取出正确的匹配对,但是需要通过反复迭代,复杂、耗时并且仍存有部分误匹配的现象。改进后的方法直接通过两条匹配直线斜率的一致性判断,剔除不在斜率范围内的匹配,此方法算法简单,省时高效,从而较大提高了特征匹配的正确率。实验结果表明,通过采用距离比率准则方法具有较高的匹配精度,同时减少了匹配的时间,使实时性得到提高。 Aiming at solving the problems of large calculating scale, poor real-time performance and high false-matching rate in the scale invariant feature transform (SIFT) feature matching algorithm,this paper presents an improved SIFT feature matching algorithm based on the distance-ratio criterion to eliminate the matching errors. Traditional random selection consistency (RANSAC) method is used to select the correct matching pairs. However, it is complex and time-consuming for the repeat iteration process as well as its occasionally matching error occurrences. In the proposed improved algorithm, slope consistency of the two matching lines is directly compared while the matching pairs beyond the slope range are eliminated. The improved algorithm is simple and efficient. Experiments show that the improved algorithm based on distance-ratio has great matching accuracy,and short matching time with performance and efficiency enhanced.
出处 《光学仪器》 2016年第6期497-500,共4页 Optical Instruments
关键词 尺度不变特征变换(SIFT) 图像匹配 随机选取一致性(RANSAC) 距离比率准则 scale invariant feature transform ( SIFT) image matching random selection
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  • 1刘臻,宫鹏,史培军.基于分层多模板匹配的影像自动配准方法研究[J].计算机应用,2005,25(2):322-325. 被引量:7
  • 2罗诗途,王艳玲,张玘,罗飞路.车载图像跟踪系统中电子稳像算法的研究[J].光学精密工程,2005,13(1):95-103. 被引量:28
  • 3丁雪梅,王维雅,黄向东.基于差分和特征不变量的运动目标检测与跟踪[J].光学精密工程,2007,15(4):570-576. 被引量:30
  • 4周颜军.数据结构[M].长春:吉林科学技术出版社,2003.
  • 5KELLER Y, AVERBUCH A, ISRAELI M. Pseu- do-polar based estimation of large translations rotations and sealings in images[J]. IEEE Transaction on Image Processing, 2005,14 (1) . 12-22.
  • 6SIGGEI.KOW S. Feature histograms for contentbased image retrieval [D].Frieiburg: Albert Lud wigs University of Frieiburg, 2002.
  • 7MIKOLAJCZYK K, SCHMID C. An affine invariant interest point detector[C]. Proceedings of the 7th European Conference on Computer Vision, 2002:128-142.
  • 8ZHANG Z Y, DERICHE R, FAUGERAS O. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry [J]. Artificial Intelligence, 1995, 78 (2):87-119.
  • 9LOWED G. Distinctive image features from scale- invariant keypoints[J]. International Journal of Computer Vision, 2004,60(2).91-110.
  • 10LINDEBERG T. Feature detection with automatic scale selection [J]. International Journal of Computer Vision, 1998,30(2) :79-116.

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