Research on Scene Matching Algorithm in the Vision-Aided Navigation System

Ya Ling Du

doi:10.4028/www.scientific.net/AMM.128-129.229

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 128-129Research on Scene Matching Algorithm in the...

Research on Scene Matching Algorithm in the Vision-Aided Navigation System

Abstract:

the maching algorithm in the vision-aided navigation system must be real-time, high-precision and suitable for matching different images with some scaling and rotation. This paper proposed three algorithms based on SURF, SIFT and ASIFT features. The SURF algorithm is the better method than the SIFT and ASIFT in theory, because the use of integral image and basic Hessian-matrix approximation has greatly reduce the computational complexity. The experimental results show that the three algorithms can obtain similar detecting and matching performance, but the SIFT and ASIFT need too much time to realize real time. So, the SURF is more suitable for scene matching in the vision-aided navigation system.

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Periodical:

Applied Mechanics and Materials (Volumes 128-129)

Pages:

229-232

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.128-129.229

Citation:

Cite this paper

Online since:

October 2011

Authors:

Ya Ling Du

Keywords:

ASIFT, Scene Matching, SIFT, SURF

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References

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

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[2] J.M. Morel and G. Yu, ASIFT: A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2(2): 438-469 (2009).

DOI: 10.1137/080732730

Google Scholar

[3] G. Yu and J.M. Morel, A Fully Affine Invariant Image Comparison Method. Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei (2009).

DOI: 10.1109/icassp.2009.4959904

Google Scholar

[4] Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc VanGool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, v. 110, n. 3, June, 2008, pp.346-359.

DOI: 10.1016/j.cviu.2007.09.014

Google Scholar

[5] Andrea Vedaldi, An implementation of SIFT detector and descriptor.

Google Scholar