A New Vanishing Point Detection Algorithm for Orthogonal Directions

Abstract:

This paper presents the orthogonal direction vanishing points detection algorithm for scene image. It use the Hough transform to detect straight lines in the image, then it based on RANSAC robust framework to calculate the initial value, the end use of Levenberg-Marquardt algorithm for nonlinear optimization iteration to strike the final vanishing point. The experiments result shows that the algorithm can not only quickly detect scene orthogonal direction vanishing points, but also high-precision and that respected this method suitable for use in a scene with large buildings or hyperopia scene.

• Periodical: Advanced Materials Research (Volumes 490-495)
• Pages: 110-114
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.490-495.110
• Online since: March 2012
• Authors: Mao Li Fu, Can Zhao, Jun Ting Cheng
• Keywords: Hough Transformation, Levenberg-Marquardt, RANSAC, Vanishing Point

References

[1] S. Amit, C. Baoquan, Peek-in-the-Pic: Flying Through Architectural Scenes from a Single Image: Computer Graphics Forum Vol. 27 (2008), p.2143–2153.

DOI: 10.1111/j.1467-8659.2008.01199.x

[2] H. Ju, Y. Wei, Y. Ming, A Self-Calibration Technique Based on the Geometry Property of the Vanish Point, Forum ACTA OPTICA SINICA, Vol. 30(2010), pp.465-472.

DOI: 10.3788/aos20103002.0465

[3] M. Straforini, C. Coelho，M. CamPani, Extraction of Vanishing Points from Images of Indoor and Outdoor Scenes，Image and Vision Computing, Vol. 11(1993), pp.91-99.

DOI: 10.1016/0262-8856(93)90075-r

[4] S.T. Barnad, Interpreting Perspective Images, Artificial Intelligence, Vol. 21(1983), pp.435-462.

[5] M. Straforini, C. Coelho, M. Campani, V. Torre, The Recovery and Understanding of a Line Drawing from Indoor Scenes. Vol. 14(1992), p.298–303.

DOI: 10.1109/34.121797

[6] T. Tuytelaars, L.V. Gool, M. Proesmans, T. Moons, The Cascaded Hough Transform as an Aid in Aerial Image Interpretation, in Proceedings of the 6th International Conference on Computer Vision, Bombay, India, (1998), pp.67-72.

DOI: 10.1109/iccv.1998.710702

[7] H. Wildenauer, M. Vincze, Vanishing Point Detection in Complex Man-made Worlds. In: ICIAP 07: Proceedings of the 14th International Conference on Image Analysis and Processing, IEEE Computer Society, Washington, DC, USA, (2007), p.615–622.

DOI: 10.1109/iciap.2007.4362845

[8] C. Rother, A New Approach for Vanishing Point Detection in Architectural Environments, in British Marching Vision Conference(BMVC), Bristol, UK, (2000), pp.382-391.

[9] H. Kong, J. Audibert, J. Ponce, Vanishing Point Detection for Road Detection. Computer Vision and Pattern Recognition, IEEE Computer Society Conference, (2009), p.96–103.

DOI: 10.1109/cvpr.2009.5206787

[10] F. Schmitt, L. Priese, Vanishing Point Detection with an Intersection Point Neighborhood. In: Discrete Geometry for Computer Imagery, (2009), p.132–143.

DOI: 10.1007/978-3-642-04397-0_12

[11] J.P. Tardif, Non-Iterative Approach for Fast and Accurate Vanishing Point Detection. In: Proceedings 12th International Conference on Computer Vision, (2009).

DOI: 10.1109/iccv.2009.5459328

[12] M.A. Fischler, R.C. Bolles, Random Sample Consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24(1981), pp.381-395.

DOI: 10.1145/358669.358692

[13] F. Schaffalitzky and A. Zisserman, Planar Grouping for Automatic Detection of Vanishing Lines and Points, Image and Vision Computing, Vol, 18(2000): pp.647-658.

DOI: 10.1016/s0262-8856(99)00069-4

[14] C. L Feng and Y. S Hung, A Robust Method for Estimating the Fundamental Matrix, Proc. 7th Digital Image Computing: (2003).

[15] C. Rother, Multi-View Reconstruction and Camera Recovery Using a Real or Virtual Reference Plane, (2003).