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

Paper Titles

Efficiency of the Determined Model of Power Consumption by Nonlinear Closed Slow-Response Production
p.561

Numerical Optimization Method of Spacecraft Antenna Retargeting Point Calculations
p.566

Electromagnetic Method for Resonance Tuning of a Mechatronic System
p.572

The Model for Estimate of Cylindrical Machined Surfaces Parameters
p.579

An Algorithm for Determining the Position and Orientation of 3-D Objects from Images Using Spectral Graph Theory
p.585

Accelerometric Method of Measuring the Angle of Rotation of the Kinematic Mechanisms of Nodes
p.592

Research and Development of Algorithms for Digital Localization of Metal Castings
p.598

The Algorithm for Generating Pairs of Projections of Three-Dimensional Objects on Two Images
p.604

Prospects of Creating Products Using Selective Laser Sintering
p.608

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.

You might also be interested in these eBooks

Urgent Problems of Up-to-Date Mechanical Engineering

View Preview

Info:

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

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - 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