An Image Matching Algorithm Based on SUSAN-SIFT Algorithm

Hui Qing Zhang; Lu Guang Cao

doi:10.4028/www.scientific.net/AMM.325-326.1588

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 325-326An Image Matching Algorithm Based on SUSAN-SIFT...

An Image Matching Algorithm Based on SUSAN-SIFT Algorithm

Abstract:

For the purpose of improving the real-time performance of the SIFT algorithm, an image matching algorithm based on SUSAN-SIFT algorithm is proposed in this paper. Use SUSAN algorithm in the detecting feature points part of the SIFT algorithm, avoiding the time-consuming down-sampling and Gaussian convolution of the algorithm, and remove the feature points of the unstable and low-contrast by the methods of interpolated estimate and the principal curvatures, and then use the SIFT algorithm to achieve the parts of describing the feature points and images matching. experimental verification: the SUSAN-SIFT algorithm has more fast calculation speed than SIFT algorithm ensuring the accuracy at the same time.

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Periodical:

Applied Mechanics and Materials (Volumes 325-326)

Pages:

1588-1592

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.325-326.1588

Citation:

Cite this paper

Online since:

June 2013

Authors:

Hui Qing Zhang, Lu Guang Cao

Keywords:

Image Matching, SIFT Algorithm, SUSAN Algorithm, SUSAN-SIFT Algorithm

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References

[1] Rafael C. Gonzalez， Richeard E. Woods and Steven L.Eddins, in: Digital Image Procession, Beijing：Publishing House of Electronics Industry(2002).

Google Scholar

[2] Brown M and Lowe D, in: Invariant features from interest point groups. In Proceedings of the 13th British Machine Vision Conference. Cardiff: [s. n.],253-262(2002).

DOI: 10.5244/c.16.23

Google Scholar

[3] Anderson J and Bedin L R and Piotto G, in: Ground-based CCD astrometry with wide field images I. Oberservations just a few years apart allow decontamination of field objects from members in two globular clusters. A & A, 1029-1045(2006).

DOI: 10.1051/0004-6361:20065004

Google Scholar

[4] Ledwich L and Williams S, in: Reduced SIFT features for image retrieval and indoor localisation. Australasian Conference on Robot-ics and Automation(2004).

Google Scholar

[5] Ke Yan and Sukthankar R, in: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. Computer Vision and Pattern Recognition. Washington D. C., USA:[s. n.](2004).

DOI: 10.1109/cvpr.2004.1315206

Google Scholar

[6] Mikolajczyk K and Schmid C, in: A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1615-1630(2005).

DOI: 10.1109/tpami.2005.188

Google Scholar

[7] Szeliski R, in: Image Alignment and Stitching: a Tutorial. Foundations and Trends in Computer Graphics and Computer Vision, 1- 104(2006).

DOI: 10.1561/0600000009

Google Scholar

[8] Yilmaz A, Javed O and Shah M, in: Object tracking: a survey. ACM Computing Surveys, 2006, 38(4): 229-240.

DOI: 10.1145/1177352.1177355

Google Scholar

[9] Hu W M, Tan T N, Wang L and Maybank S, in: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 334-352(2004).

DOI: 10.1109/tsmcc.2004.829274

Google Scholar

[10] David G. Lowe, in: Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 91-110(2004).

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar