计算机科学 ›› 2014, Vol. 41 ›› Issue (6): 282-286.doi: 10.11896/j.issn.1002-137X.2014.06.056

• 图形图像与模式识别 • 上一篇    下一篇

一种二次阈值调整SIFT算法

卫保国,张海曦   

  1. 西北工业大学电子信息学院 西安710129;西北工业大学电子信息学院 西安710129
  • 出版日期:2018-11-14 发布日期:2018-11-14

Twofold Adjusted Threshold SIFT

WEI Bao-guo and ZHANG Hai-xi   

  • Online:2018-11-14 Published:2018-11-14

摘要: SIFT算法因其良好的特征提取和匹配效果得到了广泛的应用,但在光照不足和模糊条件下其效果不能令人满意,为此提出了一种基于全局信息和局部信息的自适应SIFT算法。利用图像的对比度信息得到初始阈值,使该阈值适应光照不足和 模糊图像,根据周围特征点分布情况来对阈值进行二次调整以控制特征点数目及分布,并改进了误匹配剔除方法。实验结果表明,改进后的SIFT算法不仅能很好地适应低光照和模糊图像,而且可以调节特征点数目,降低簇效应。

关键词: SIFT,阈值,图像匹配 中图法分类号TP301文献标识码A

Abstract: SIFT has been widely used for its good performance on feature extraction and matching.However,effects under conditions of insufficient illumination and blur are not so satisfactory.We proposed an adaptive threshold selection method based on global and local information.First,initial threshold can be obtained according to image contrast,thus it’s adapted to insufficient illumination and image blur.Second,in order to control the number and distribution of feature points,the threshold is adjusted secondly according to feature points distribution.Finally,the mismatch removing method has also been improved.Experiment results show that the improved SIFT algorithm is not only well adapted to low light and image blur,but also can adjust feature point numbers and reduce clustering effects.

Key words: SIFT,Threshold,Image matching

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