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Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis

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Abstract

This paper aims to explore frequency behavior of isotropic (regular SIFT) and anisotropic (Bi-SIFT and Tri-SIFT) versions of the scale-space keypoint detection algorithm SIFT. We introduced a new smoothing function Trilateral filter that can be used in formation of a scale-space as an alternative to the Gaussian scale-space. The number of matching pixels, warping error, and scatteredness are employed in comparison. We made the comparison out of face dataset and object dataset for scale, orientation, and view-angle transformations as well as lighting and compression variations. The comparison results show that anisotropic smoothing detects more keypoints than isotropic one. The Tri-SIFT is more robust to variation in viewpoint angle.

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References

  1. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  2. Lindeberg, T.: Scale-space theory: a basic tool for analysing structures at different scales. J. Appl. Stat. 21(2), 225–270 (1994)

    Article  Google Scholar 

  3. Koenderink, J.J.: The structure of images. Biol. Cybern. 50(5), 363–370 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  4. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: International Conference on Computer Vision, Bombay (1998)

  5. Mikolajczyk, K.: Detection of local features invariant to affine transformations. Ph.D. thesis, Institut National Polytechnique de Grenoble, France (2002)

  6. Wu, J., Cui, Z., Sheng, V.S., Zhao, P., Su, D., Gong, S.: A comparative study of SIFT and its variants. Meas. Sci. Rev. 13(3), 122–131 (2013)

    Article  Google Scholar 

  7. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2004)

  8. Mortensen, E.N., Deng, H., Shapiro, L.: A SIFT descriptor with global context. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (2005)

  9. Abdel-Hakim, A.E., Farag, A.A.: CSIFT: A SIFT descriptor with color invariant characteristics. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) (2006)

  10. Morel, J.M., Yu, G.: ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2), 438–469 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  11. Skoch, W., Gauch, J.: The effects of anisotropic Gaussian diffusion in scale invariant feature detection. In: IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011), Kuala Lumpur (2011)

  12. Wang, F.: Adapted anisotropic Gaussian SIFT matching strategy for SAR registration. Geosci. Remote Sens. Lett. 12(1), 160–164 (2015)

    Article  Google Scholar 

  13. Alcantarilla, P.F., Davison, A.B., Andrew, J.: KAZE features. In: In European Conference on Computer Vision (ECCV), Fiorenze, Italy (2012)

  14. Wang, S., Fu, H.Y.K.: BFSIFT: a novel method to find feature matches for SAR image registration. Geosci. Remote Sens. Lett. 9(4), 649–653 (2012)

    Article  Google Scholar 

  15. Huang, M., Mu, Z., Zeng, H., Huang, H.: A novel approach for interest point detection via Laplacian-of-bilateral filter. J. Sens. 2015, 9 (2015)

    Google Scholar 

  16. Databases for Face Detection and Pose Estimation. http://robotics.csie.ncku.edu.tw/Databases/FaceDetect_PoseEstimate.htm#Our_Database_

  17. Chen, J.C., Lien, J.J.J.: A view-based statistical system for multi-view face detection and pose estimation. Image Vis. Comput. 27(9), 1252–1271 (2009)

    Article  Google Scholar 

  18. Affine Covariant Features. http://www.robots.ox.ac.uk/~vgg/research/affine/

  19. Mikolajczyk, T.T.K., Schmid, A.Z.C., Matas, F.S.J., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1), 43–72 (2005)

    Article  Google Scholar 

  20. http://caitlinsg324mediablog.blogspot.com/2012_12_01_archive.html

  21. https://www.ephotozine.com/articles/nikon-coolpix-p510-digital-compact-camera-review-18761/images/330-nikoncoolpixp510zoomexample1_1331888105.jpg

  22. http://users.isr.ist.utl.pt/~aguiar/kids.jpg

  23. https://c1.staticflickr.com/5/4001/4648662998_5ef1a56fa3_z.jpg

  24. http://designerseries.nz/~nzbrickco/sites/default/files/nzbrick-designer%E2%80%93series-light-weight-panels-textured-swatch.jpg

  25. http://www.fun-lover.com/graphic-shop/Textures/images/RocksStone/rkt-045.jpg

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Correspondence to Ömer Muhammet Soysal.

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Şekeroğlu, K., Soysal, Ö.M. Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis. Machine Vision and Applications 28, 875–902 (2017). https://doi.org/10.1007/s00138-017-0868-9

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  • DOI: https://doi.org/10.1007/s00138-017-0868-9

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