Computer Vision_33_SIFT：An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images——2015

此部分是计算机视觉部分，主要侧重在底层特征提取，视频分析，跟踪，目标检测和识别方面等方面。对于自己不太熟悉的领域比如摄像机标定和立体视觉，仅仅列出上google上引用次数比较多的文献。有一些刚刚出版的文章，个人非常喜欢，也列出来了。

33. SIFT
关于SIFT，实在不需要介绍太多，一万多次的引用已经说明问题了。SURF和PCA-SIFT也是属于这个系列。后面列出了几篇跟SIFT有关的问题。
[1999 ICCV] Object recognition from local scale-invariant features
[2000 IJCV] Evaluation of Interest Point Detectors
[2006 CVIU] Speeded-Up Robust Features (SURF)
[2004 CVPR] PCA-SIFT A More Distinctive Representation for Local Image Descriptors
[2004 IJCV] Distinctive Image Features from Scale-Invariant Keypoints

[2009 GRSL] Robust scale-invariant feature matching for remote sensing image registration
[2010 IJCV] Improving Bag-of-Features for Large Scale Image Search
[2011 PAMI] SIFTflow Dense Correspondence across Scenes and its Applications

[2014 CVPR] TILDE: A Temporally Invariant Learned DEtector

[2015 GRSL] An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images

[2015 TGRS] SAR-SIFT: A SIFT-LIKE ALGORITHM FOR SAR IMAGES

[2017 GRSL] Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching

[2017 CVPR] GMS :Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence

 

翻译

一种基于SIFT的高效模式寻找算法，用于遥感图像的亚像素配准

作者：Benny Kupfer, Nathan S. Netanyahu

 

 

参考资料

[1] D.Lowe,“Distinctiveimagefeaturesfromscale-invariantkeypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.
[2] N. S. Netanyahu, J. Le Moigne, and J. G. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 7, pp. 1586–1600, Jul. 2004.
[3] D. M. Mount, N. S. Netanyahu, and S. Ratanasanya, “New approaches to robust, point-based image registration,” in Image Registration for Remote Sensing, J. LeMoigne, N. S. Netanyahu, and R. D. Eastman, Eds. Cambridge, U.K.: Cambridge Univ. Press, Mar. 2011.
[4] Q. Li, G. Wang, J. Liu, and S. Chen, “Robust scale-invariant feature matching for remote sensing image registration,” IEEE Geosci. Remote Sens. Lett., vol. 6, no. 2, pp. 287–291, Apr. 2009.
[5] M.Teke,M.F.Vural,A.Temizel,andY.Yardımcı,“High-resolutionmultispectral satellite image matching using scale invariant feature transform and speeded up robust features,” J. Appl. Remote Sens., vol. 5, no. 1, pp. 053553-1–053553-9, Jan. 2011.
[6] A. Sedaghat, M. Mokhtarzade, and H. Ebadi, “Uniform robust scaleinvariant feature matching for optical remote sensing images,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4516–4527, Nov. 2011.
[7] Q. Li, H. Zhang, and T. Wang, “Multispectral image matching using rotation-invariant distance,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 3, pp. 406–410, May 2011.
[8] M. Fischler and R. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM, vol. 24, no. 6, pp. 381–395, Jun. 1981.
[9] M. Hasan, X. Jia, A. Robles-Kelly, J. Zhou, and M. R. Pickering, “Multispectralremotesensingimageregistrationviaspatialrelationshipanalysis on sift keypoints,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2010, pp. 1011–1014.
[10] M. Hasan, M. R. Pickering, and X. Jia, “Modified SIFT for multi-modal remote sensing image registration,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2012, pp. 2348–2351.
[11] B. Kupfer, “A SIFT-based image registration algorithm for remotely sensed data,” M.Sc. thesis, Bar-Ilan Univ., Ramat-Gan, Israel, 2013. [Online]. Available:www.cs.biu.ac.il/~nathan/registration/kupfer_thesis. pdf