Attempts at Automatic Detection of Railway Head Edges from Images and Laser Data

• Autorzy: Marmol U. , Mikrut S.
• Języki publikacji: EN

Abstrakty

EN

The more and more high resolution of aerial and ground images, as well as high density of laser data cause that they are more and more widely applied in many engineering projects. Given the current technical parameters, it is also possible to map railway infrastructure not only from the ground level but also from airborne locations (photogrammetry, laser scanning). Testing the usefulness of those data in obtaining information about railway infrastructure, and in particular, in detecting rail heads has been a subject of research of this paper authors. The paper presents results of experiments, consisting in verification of existing solutions and testing own algorithms for an automatic extraction of railway rail heads. The tested algorithms of object detecting and locating produced preliminary, satisfying results. The authors believe it to be reasonable to continue their research work.

Słowa kluczowe

EN

aerial photogrammetry; laser scanning; ALS

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2012
• Tom: Vol. 17, no. 4
• Strony: 151--160
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Marmol U.

• [e-mail]
• AGH University of Science and Technology, Department of Geoinformation, Photogrammetry and Remote Sensing of Environment

autor

Mikrut S.

• AGH University of Science and Technology, Department of Geoinformation, Photogrammetry and Remote Sensing of Environment

Bibliografia

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• [4] M. Arastounia, Automatic classification of object from lidar point clouds in a railway environment, MSc, Thesis, University of Twenty, 2012
• [5] R. Beger, C. Gedrange, R. Hecht, M. Neubert, Data fusion of extremely high resolution aerial imagery and LIDAR data for automated railroad centre line reconstruction, ISPRS Journal of Photogrammetry and Remote Sensing 66 (6, Supplement), pp. 40-51, 2011
• [6] Terrasolid, www.terrasolid.fi accessed 1.VI.2012
• [7] M.A. Fischler, R.C. Bolles, Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Comm. of the ACM 24, No. 6, pp. 381-395, 1981
• [8] Ch. Sunglok, K. Taemin, Y. Wonpil, Performance Evaluation of RANSAC Family, BMVC 2009
• [9] P. Kovesi, http://www.csse.uwa.edu.au/~pk/research/matlabfns/#robust, accessed 1.VI.2012
• [10] S. Mikrut, The influence of scanning and JPEG compression on linear and points feature extraction on digital images, Ph. D. Thesis, AGH, 2003