A SIFT Feature Matching Algorithm Based on Semi-Variance Function

Feng Tian; Yu Bo Yan

doi:10.4028/www.scientific.net/AMR.647.896

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 647A SIFT Feature Matching Algorithm Based on...

A SIFT Feature Matching Algorithm Based on Semi-Variance Function

Abstract:

For solving the low matching efficiency problem due to high dimension of eigenvector in SIFT, a SIFT feature matching algorithm based on semi-variance function is proposed. For each feature point in image SIFT feature point zone, m beams are generated by using the position of the feature point as center and the orientation of the feature point as start direction. The image semi-variance function value of each beam, which is treated as SIFT value of eigenvector descriptor, is used in the algorithm aiming at reducing the dimension of eigenvector and improving image matching efficiency. The experiment result shows that the matching rate of this algorithm is higher, the matching time of this algorithm is less.

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Periodical:

Advanced Materials Research (Volume 647)

Pages:

896-900

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.647.896

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Online since:

January 2013

Authors:

Feng Tian, Yu Bo Yan

Keywords:

Feature Descriptor, Feature Matching, Semi-Variance Function, SIFT Algorithm

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References

[1] D. Lowe: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision . Vol. 60 (2004), pp.91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[2] K. Mikolajczyk , C. Schmid: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 27 (2005), pp.1615-1630.

DOI: 10.1109/tpami.2005.188

Google Scholar

[3] W. Wang , J. Hong and Y. Tang: Image Matching for Geomorphic Measurement Based on SIFT and RANSAC Methods. International Conference on Computer Science and Software Engineering. Vol. 2 (2008), pp.317-320.

DOI: 10.1109/csse.2008.318

Google Scholar

[4] K. Yan, R. Sukthankar: PCA-SIFT: a more distinctive representation for local image descriptors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2 (2004), pp.506-513.

DOI: 10.1109/cvpr.2004.1315206

Google Scholar

[5] Y. Hua, J. Lin and C. Lin: An improved SIFT feature matching algorithm. The 8th World Congress on Intelligent Control and Automation. (2010), pp.6109-6113.

DOI: 10.1109/wcica.2010.5554659

Google Scholar