Reconstructing power cables from LIDAR data using Eigenvector streamlines of the point distribution tensor field

Journal of WSCG

Volumn 20, Issue 3, 2012, Pages 223-230

  Ritter, Marcel ^b   Benger, Werner ^a,b  

^a LOUISIANA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

Covariance matrix; DT MRI; Fiber tracking; LIDAR; Mesh free methods; Particle systems; Point cloud; Streamlines; Tensor field visualization

Indexed keywords

EID: 84867181767     PISSN: 12136972     EISSN: 12136964     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. W. Benger, H. Bartsch, H.-C. Hege, H. Kitzler, A. Shumilina, and A. Werner. Visualizing Neuronal Structures in the Human Brain via Diffusion Tensor MRI. International Journal of Neuroscience, 116(4):pp. 461-514, 2006.
2. th High-End Visualization Workshop, Obergurgl, Tyrol, Austria, June 18-21, 2007, pages 26-39. Berlin, Lehmanns Media-LOB. de, 2007.
3. W. Benger and M. Ritter. Using geometric algebra for visualizing integral curves. In E. M. S. Hitzer and V. Skala, editors, GraVisMa 2010-Computer Graphics, Vision and Mathematics for Scientific Computing. Union Agency-Science Press, 2010.
4. th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pages 117-124, 2009.
5. T. E. Conturo, N. F. Lori, T. S. Cull, E. Akbudak, A. Z. Snyder, J. S. Shimony, R. C. Mckinstry, H. Burton, and M. E. Raichle. Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A, 96(18):10422-10427, Aug. 1999.
6. M. Descoteaux, L. Collins, and K. Siddiqi. A multi-scale geometric flow for segmenting vasculature in mri. In In Medical Imaging Computing and Computer-Assisted Intervention, pages 500-507, 2004.
7. S. N. E. Hairer and G. Wanner. Solving ordinary differential equations I, nonstiff problems, 2nd edition. Springer Series in Computational Mathematics, Springer-Verlag, 1993.
8. L. A. Fernandes and M. M. Oliveira. Realtime line detection through an improved hough transform voting scheme. Pattern Recognition, 41(1):299-314, 2008.
9. K. Fukunaga and L. Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory, IEEE Transactions on, 21(1):32-40, jan 1975.
10. D. A. Fulk and D. W. Quinn. An analysis of 1D smoothed particle hydrodynamics kernels. Journal of Computational Physics, 126(1):165-180, 1996.
11. R. C. Gonzalez and R. E. Woods. Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition, 2001.
12. D. K. Jones. Studying connections in the living human brain with diffusion mri. Cortex, 44(8):936-952, 2008.
13. Y. Jwa, G. Sohn, and H. B. Kim. Automatic 3d powerline reconstruction using airborne lidar data. IAPRS, XXXVIII(2004):105-110, 2009.
14. K. Kraus and N. Pfeifer. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53(4):193-203, 1998.
15. Z. Li, R. Walker, R. Hayward, and L. Mejias. Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques. In Applied Robotics for the Power Industry (CARPI), 2010 1st International Conference on, pages 1-6, oct. 2010.
16. Y. Liu, Z. Li, R. F. Hayward, R. A. Walker, and H. Jin. Classification of airborne lidar intensity data using statistical analysis and hough transform with application to power line corridors. In Digital Image Computing: Techniques and Applications Conference (DICTA 2009), Melbourne, Victoria, December 2009. IEEE Computer Society.
17. T. Melzer. Non-parametric segmentation of als point clouds using mean shift. Journal of Applied Geodesy, 1(3):159-170, 2007.
18. T. Melzer and C. Briese. Extraction and modeling of power lines from als point clouds. In Proceedings of 28thWorkshop, pages 47-54. Österreichische Computer Gesellschaft, 2004. talk: Austrian Association for Pattern Recognition (ÖAGM), Hagenberg; 2004-06-17-2004-06-18.
19. S. Mills, M. Gerardo, Z. Li, J. Cai, R. F. Hayward, L. Mejias, and R. A. Walker. Evaluation of aerial remote sensing techniques for vegetation management in power line corridors. IEEE Transactions on Geoscience and Remote Sensing, October 2009.
20. M. Persson, J. Solem, K. Markenroth, J. Svensson, and A. Heyden. Phase contrast mri segmentation using velocity and intensity. In R. Kimmel, N. Sochen, and J. Weickert, editors, Scale Space and PDE Methods in Computer Vision, volume 3459 of Lecture Notes in Computer Science, pages 119-130. Springer Berlin-Heidelberg, 2005.
21. M. Ritter, W. Benger, B. Cosenza, K. Pullman, H. Moritsch, and W. Leimer. Visual data mining using the point distribution tensor. In IARIS Workshop on Computer Vision and Computer Graphics-VisGra 2012, Feb-Mar 2012.
22. F. Steinbacher, M. Pfennigbauer, A. Ulrich, and M. Aufleger. Vermessung der Gewassersohle-aus der Luft-durch das Wasser. In Wasserbau in Bewegung. von der Statik zur Dynamik. Beitrage zum 15. Gemeinschaftssymposium der Wasserbau Institute TU München, TU Graz und ETH Zürich, 2010.
23. D. Weinstein, G. Kindlmann, and E. Lundberg. Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In Proceedings of the conference on Visualization '99: celebrating ten years, VIS '99, pages 249-253, Los Alamitos, CA, USA, 1999. IEEE Computer Society Press.
24. C. Westin, S. Peled, H. Gudbjartsson, R. Kikinis, and F. Jolesz. Geometrical diffusion measures for mri from tensor basis analysis. In Proceedings of ISMRM, Fifth Meeting, Vancouver, Canada, page 1742, Apr. 1997.