Analysis and Proof for Convergence Region of Vision Iterative Method

Li Juan Qin; Yu Lan Hu

doi:10.4028/www.scientific.net/AMR.186.650

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 186Analysis and Proof for Convergence Region of...

Analysis and Proof for Convergence Region of Vision Iterative Method

Abstract:

We have presented a new vision solution method which has good real-time characteristic. It satisfies the convergence characteristic in some regions, which is the premise for running of our solution method. In this paper, we present one convergence region and provide a detailed account of proof for the convergence region. Proof for convergence region can provide theoretical basis for vision solution method.

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Periodical:

Advanced Materials Research (Volume 186)

Pages:

650-654

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.186.650

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Online since:

January 2011

Authors:

Li Juan Qin, Yu Lan Hu

Keywords:

Convergence Region, Iterative, Vision

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References

[1] Young-ran Lee: Pose Estimation of Linear Cameras Using Linear Feature (Dissertation Publications, The Ohio State University 2002).

Google Scholar

[2] Thai quynh phong, in: Object Pose from 2-D to 3-D Point and Line Correspondences. volume 15 of International Journal of Computer Vision, (1995).

Google Scholar

[3] J S-C Yuan, in: A General Photogrammetric Method for Determining Object Position and Orientation. volume 5 of IEEE Transactions on Robotics and Automation, chapter, 2, (1989).

DOI: 10.1109/70.88034

Google Scholar

[4] D Lowe, in: Three-dimensional Object Recognition from Single Two-dimensional Images. volume 31 of Artificial Intelligence, (1987).

DOI: 10.1016/0004-3702(87)90070-1

Google Scholar

[5] Y Liu, T S Huang, and O D Faugeras, in: Determination of Camera Location from 2-D to 3-D Line and Point Correspondences. Volume 12 of IEEE Transanctions on Pattern Analysis and Machine Intelligence, chapter, 1, (1990).

DOI: 10.1109/34.41381

Google Scholar

[6] Stephane Christy and Radu Horaud, in: Iterative Pose Computation from Line Correspondences. volume 73 of Computer Vision and Image Understanding, chapter, 1, (1999).

DOI: 10.1006/cviu.1998.0717

Google Scholar

[7] Qin Lijuan, Zhu Feng, in: A Novel Algorithm for Real-time Pose Determination and Its Iterative Method, IEEE International Conference on Information and Acquirement, (2006).

DOI: 10.1109/icia.2006.306018

Google Scholar

[8] Qin Lijuan, Yulan Hu and etc, in: Research on Monotony Character of A New Location Method, Chinese Control and Decision Conference, (2008).

DOI: 10.1109/ccdc.2008.4597311

Google Scholar