Implementation of Image Matching Algorithm Based on SIFT Features

Mu Yi Yin; Fei Guan; Peng Ding; Zhong Feng Liu

doi:10.4028/www.scientific.net/AMM.602-605.3181

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Implementation of Image Matching Algorithm Based...

Implementation of Image Matching Algorithm Based on SIFT Features

Abstract:

With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

3181-3184

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.3181

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Online since:

August 2014

Authors:

Mu Yi Yin, Fei Guan*, Peng Ding, Zhong Feng Liu

Keywords:

(SIFT), Feature Extraction, Image Matching, Scale Space

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* - Corresponding Author

References

[1] Zhang Shaohui, Shen Xiaorong, Fan Yaozu. Method in image's feature extraction and matching[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(5): 516-519.

Google Scholar

[2] YANG Xiao-min, WU Wei, QING Lin-bo, et al. Image feature extraction and matching technology[J]. Optics and Precision Engineering, 2009, 17(9): 2276-2281.

Google Scholar

[3] David G. Lowe. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[4] Rob Hess. An Open-Source SIFT Library[C]. Proceedings of the international conference on Multimedia. Firenze, Italy, 2010: 1493-1496.

Google Scholar

[5] Mikolajczyk, K. Detection of local features invariant to affine transformations[D]. Institute National Polytechnique de Grenoble, France, 2002: 1-40.

Google Scholar

[6] Yosi Keller, Amir Averbuch, Moshe Israeli. Pseudo-polar based estimation of large translations, rotations, and scalings in images[J]. IEEE Transaction on Image Processing , 2005, 14(1): 12-22.

DOI: 10.1109/tip.2004.838692

Google Scholar

[7] Siggelkow S. Feature histograms for content based image retrieval[D]. Frieiburg: Albert Ludwigs University of Frieiburg , 2002: 1-40.

Google Scholar

[8] Mikolajczyk, K. Schmid C. An affine invariant interest point detector[C]. Proceedings o f the 7th European Conference on Computer Vision, 2002: 128-142.

DOI: 10.1007/3-540-47969-4_9

Google Scholar