计算机科学 ›› 2014, Vol. 41 ›› Issue (6): 282-286.doi: 10.11896/j.issn.1002-137X.2014.06.056
卫保国,张海曦
WEI Bao-guo and ZHANG Hai-xi
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。
[1] Lowe D.Object recognition from local scale-invariant features[C]∥Proceeding of ICCV.Piscataway,NJ,USA:Institute of Electrical and Electronics Engineers Inc.,1999,2:1150-1157 [2] Lowe D.Distinctive image features from scale-invariant key-point [J].International Journal of Computer Vision,2004,0(2):91-110 [3] Bay H,Tuytelaars T,Gool L V.SURF:Speeded up Robust Features [C]∥European Conference on Computer Vision.Berlin,Heidlberg:Sprin-gerVerlag,2006:404-417 [4] Ke Y,Sukthankar R.PCA-SIFT:A more distinctive representation for local image descriptors[C]∥Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.Washington,DC,USA,2004:511-517 [5] Morel J M,Yu G S.A-SIFT:A new framework for fully affine invariant image comparison [J].Society for Industrial and applied Mathematics Journal on Image Sciences,2009,2(2):438-469 [6] Song R,Szymanski J.Well-distributed SIFT features[J].Electronics Letters,2009,45(6):308-310 [7] Zhai You,Zeng Luan.A SIFT matching algorithm based on adaptive contrast threshold[C]∥International Conference on Consumer Electronics,Communications and Networks.Xianing,China,2011:1934-1937 [8] Fischler M,Bolles R.Random sample consensus:A paradigm formodel fitting with applications to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395 [9] Li Peng,Yan Han-bing,Cui Gang,et al.Image local invariantfeatures matching using global information[C]∥International Conference on Information Science and Technology.Hubei,China,2012:627-633 |
No related articles found! |
|