Abstract
In view of the feature points extracted by the SIFT algorithm can not fully represent the structure of the object and the computational complexity is high, an improved Harris-SIFT image matching algorithm is proposed. Firstly, the feature points of the image are extracted by Harris corner detection operator. Then, the feature points are described by using the 28 dimension increasing homocentric square window. Euclidean distance is used as the similarity measure function in the matching process. Finally, simulation results show the validity of the improved algorithm, providing a new thought for the research into the image matching.
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References
Zhou, R., Dexing, D., Han, J.: Fingerprint identification using SIFT-based minutia descriptors and improved all descriptor-pair matching. Sensors 13(3), 3142–3156 (2013)
Guo, Y., Sohel, F., Bennamoun, M., et al.: An accurate and robust range image registration algorithm for 3D object modeling. IEEE Trans. Multimedia 16(5), 1377–1390 (2014)
Chen, Y., Shang, L.: Improved SIFT image registration algorithm on characteristic statistical distributions and consistency constraint. Optik-Int. J. Light Electr. Opt. 127(2), 900–911 (2016)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, no. 2, pp. 506–513 (2004)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of International Conference on Computer Vision, pp. 1150–1157 (1999)
Gal, R., Cohen-Or, D.: Salient geometric features for partial shape matching and similarity. ACM (2006)
Wang, Y., Hu, J., Han, F.: Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Elsevier Science Inc. (2007)
Maintz, J.B.A., van den Elsen, P.A., Viergever, M.A.: Evaluation of ridge seeking operators for multimodality medical image matching. IEEE Trans. Pattern Anal. Mach. Intell. 18(4), 353–365 (2008)
Er-Sen, L.I., Zhang, B.M., Liu, J.Z., et al.: The application of SIFT feature matching method in the automatic relative orientation. Sci. Surv. Mapp. 33(5), 15–16 (2008)
Tian, F., Yan, Y.B.: A SIFT feature matching algorithm based on semi-variance function. Adv. Mater. Res. 647, 896–900 (2013)
Zhao, J., Xue, L.J., Men, G.Z.: Optimization matching algorithm based on improved Harris and SIFT. In: International Conference on Machine Learning and Cybernetics, pp. 258–261. IEEE (2010)
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This paper is supported by the project of young creative talents training program of Heilongjiang undergraduate higher education institution (UNPYSCT-2015039).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Cao, Y., Pang, B., Liu, X., Shi, Yl. (2018). An Improved Harris-SIFT Algorithm for Image Matching. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_8
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DOI: https://doi.org/10.1007/978-3-319-73317-3_8
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