An Algorithm for Determining the Position and Orientation of 3-D Objects from Images Using Spectral Graph Theory

Alexey Barinov; Aleksey Zakharov

doi:10.4028/www.scientific.net/AMM.770.585

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 770An Algorithm for Determining the Position and...

An Algorithm for Determining the Position and Orientation of 3-D Objects from Images Using Spectral Graph Theory

Abstract:

This paper describes an algorithm for computing the position and orientation of 3-D objects by comparing graphs. The graphs are based on feature points of the image. Comparison is performed by a spectral decomposition with obtaining eigenvectors of weighted adjacency matrix of the graph.

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Periodical:

Applied Mechanics and Materials (Volume 770)

Pages:

585-591

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.770.585

Citation:

Cite this paper

Online since:

June 2015

Authors:

Alexey Barinov*, Aleksey Zakharov

Keywords:

Bridge Crane, Eigen Decomposition, Orientation, Position, Spectral Graph Theory

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* - Corresponding Author

References

[1] D. Cvetkovic, M. Doob, H. Sachs, Spectra of Graphs. Theory and Application, VEB Deutscher Verlag der Wissenschaften, Berlin, (1980).

Google Scholar

[2] N. Dey, P. Nandy, N. Barman, D. Das, A Comparative Study between Moravec and Harris Corner Detection of Noisy Images Using Adaptive Wavelet Thresholding Technique, 2 (2012) 599-606.

Google Scholar

[3] J. Shi, C. Tomasi, Good feature to track, CVPR94, (1994) 593-600.

Google Scholar

[4] E. Rosten, R. Porter, T. Drummond, Faster and better: a machine learning approach to corner detection, IEEE Trans. J. Pattern Analysis and Machine Intelligence 32 (2010) 105-119.

DOI: 10.1109/tpami.2008.275

Google Scholar

[5] M. Haseeb, Spectral Representation for Matching and Recognition, (2013).

Google Scholar

[6] S. Umeyama, An eigen decomposition approach to weighted graph matching problems, IEEE Trans. Pattern Anal. J. Machine Intell, 10 (1988), 695-703.

DOI: 10.1109/34.6778

Google Scholar

[7] L. Shapiro, J. Brady, Feature-base correspondence: an eigenvector approach, J. Image Vision Comput. 10 (1992) 283-288.

DOI: 10.1016/0262-8856(92)90043-3

Google Scholar

[8] M. Carcassoni, E.R. Hancock, Correspondence Matching with Modal Clusters, IEEE Trans. J. On PAM, 25 (2003) 1609-(2003).

DOI: 10.1109/tpami.2003.1251153

Google Scholar

[9] M. Carcassoni, E.R. Hancock, Spectral correspondence for point pattern matching, J. Pattern Recognition. 36 (2003) 193-204.

DOI: 10.1016/s0031-3203(02)00054-7

Google Scholar