Abstract
The SIFT (scale invariant feature transform) key-point serves as an indispensable role in many computer vision applications. This paper presents an approximation of the SIFT scale space for key-point detection with high efficiency while preserving the accuracy. We build the scale space by repeated averaging filters to approximate the Gaussian filters used in SIFT algorithm. The accuracy of the proposed method is guaranteed by that an image undergoes repeated smoothing with an averaging filter is approximately equivalent to the smoothing with a specified Gaussian filter, which can be proved by the center limit theorem. The efficiency is improved by using integral image to fast compute the averaging filtering. In addition, we also present a method to filter out unstable key-points on the edges. Experimental results demonstrate the proposed method can generate high repeatable key-points quite close to the SIFT with only about one tenth of computational complexity of SIFT, and concurrently the proposed method does outperform many other methods.
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References
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
St-Charles, P. L., Bilodeau, G. A., & Bergevin, R. (2016). Fast image gradients using binary feature convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1–9).
Loncomilla, P., Ruiz-del-Solar, J., & Martnez, L. (2016). Object recognition using local invariant features for robotic applications: A survey. Pattern Recognition, 60, 499–514.
Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., et al. (2016). Remote sensing image registration with modified SIFT and enhanced feature matching. IEEE Geoscience and Remote Sensing Letters, 14(1), 3–7.
Yang, C., Wanyu, L., Yanli, Z., & Hong, L. (2016). The research of video tracking based on improved SIFT algorithm. In 2016 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 1703–1707). Piscataway, NJ: IEEE.
Stcharles, P. L., Bilodeau, G. A., & Bergevin, R. (2016). Fast image gradients using binary feature convolutions. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1074–1082). Piscataway, NJ: IEEE.
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.
Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). Brief: Binary robust independent elementary features. In European Conference on Computer Vision (pp. 778–792). Berlin: Springer.
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. R. (2011). ORB: An efficient alternative to SIFT or SURF. In 2011 IEEE International Conference on Computer Vision (ICCV) (pp. 2564–2571). Piscataway, NJ: IEEE.
Leutenegger S, Chli M, Siegwart R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In 2011 IEEE International Conference on Computer Vision (ICCV) (pp. 2548–2555). Piscataway, NJ: IEEE.
Alahi, A., Ortiz, R., & Vandergheynst, P. (2012). Freak: Fast retina keypoint. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 510–517). Piscataway, NJ: IEEE.
Mitsugami, I. (2013). Bundler: Structure from motion for unordered image collections. Journal of the Institute of Image Information & Television Engineers, 65(4), 479–482.
Agrawal, M., Konolige, K., & Blas, M. R. (2008). CenSurE: Center surround extremas for realtime feature detection and matching. In European Conference on Computer Vision (ECCV) (pp. 102–115). Berlin: Springer.
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004). Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 22(10), 761–767.
Grabner, M., Grabner, H., & Bischof, H. (2006). Fast approximated SIFT. In Asian Conference on Computer Vision (pp. 918–927). Berlin: Springer.
Crowley, J. L., & Parker, A. C. (1984). A representation for shape based on peaks and ridges in the difference of low-pass transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(2), 156–170.
Lindeberg, T. (1994). Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics, 21(2), 225–270, 21(2):224–270.
Hess, R. (2010). An open-source SIFTLibrary. In Proceedings of the Inter National Conference on Multimedia (pp. 1493–1496). New York, NY: ACM.
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., et al. (2005). A comparison of affine region detectors. International Journal of Computer Vision, 65(1–2), 43–72.
Acknowledgements
This work is supported by NSFC under Grant 61860206007 and 61571313, by National Key Research and Development Program under Grant 2016YFB0800600 and 2016YFB0801100 and by funding from Sichuan Province under Grant 18GJHZ0138, and by joint funding from Sichuan University and Lu-Zhou city under 2016CDLZ-G02-SCU.
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Wang, Y., Liu, Y., Xu, Z., Zheng, Y., Hong, W. (2019). Approximated Scale Space for Efficient and Accurate SIFT Key-Point Detection. In: Quinto, E., Ida, N., Jiang, M., Louis, A. (eds) The Proceedings of the International Conference on Sensing and Imaging, 2018. ICSI 2018. Lecture Notes in Electrical Engineering, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-030-30825-4_3
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DOI: https://doi.org/10.1007/978-3-030-30825-4_3
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