Open Access
| Issue |
MATEC Web Conf.
Volume 271, 2019
2019 Tran-SET Annual Conference
|
|
|---|---|---|
| Article Number | 08004 | |
| Number of page(s) | 5 | |
| Section | Pavements | |
| DOI | https://doi.org/10.1051/matecconf/201927108004 | |
| Published online | 09 April 2019 | |
- Yun, H-B., Mokhtari. S., and Wu, L. (2015). Crack recognition and segmentation using morphological image-processing techniques for flexible pavements. Transportation Research Record: Journal of the Transportation Research Board 2523: 115–124. [CrossRef] [Google Scholar]
- Chambon, S., and Moliard, J-M., (2011). Automatic road pavement assessment with image processing: review and comparison. International Journal of Geophysics. [Google Scholar]
- Svasdisant, T., Schorch, M., and Baladi, G.Y. (2002). Mechanistic analysis of top-down cracks in asphalt pavements. Paper presented in Transportation Research Board Annual Meeting, January, 13–17. [Google Scholar]
- Myers, L.A., Roque, R., and Ruth, B.E., (1998). Mechanisms of surface-initiated longitudinal wheel path cracks in high-type bituminous pavements. Journal of the Association of Asphalt Paving Technologists, 67, 401–432. [Google Scholar]
- Uhlmeyer, J.S., Willoughby, K., Pierce, L.M., and Mahoney, J.P. (2000). Top-down cracking in Washington state asphalt concrete wearing course. In Transportation Research Record: Journal of Transportation Research Board, No. 1730, TRB, National Research Council, Washington, DC, 110–116. [CrossRef] [Google Scholar]
- Maser, K.R. (1987). Computational techniques for automating visual inspection. Massachusetts Institute of Technology, Report, Cambridge, MA. [Google Scholar]
- Georgopoulos, A., Loizos, A., and Flouda, A. (1995). Digital image processing as a tool for pavement distress evaluation. ISPRS Journal of Photogrammetry and Remote Sensing, 50(1), pp. 23–33. [CrossRef] [Google Scholar]
- Xu, B. and Huang, Y.R. (2003). Development of an automatic pavement surface distress inspection system. No. FHWA/TX-05/7-4975-1. [Google Scholar]
- Ying, L., and Salari, E. (2009). Beamlet transform based technique for pavement image processing and classification. In Electro/Information Technology, 2009. eit’09. IEEE International Conference, 141–145. [Google Scholar]
- Wu, L., Mokhtari, S., Nazef, A., Nam, B., and Yun, H-B. (2014). Improvement of crack-detection accuracy using a novel crack defragmentation technique in image-based road assessment. Journal of Computing in Civil Engineering, 30(1): 04014118. [CrossRef] [Google Scholar]
- Mokhtari, S., Wu, L., and Yun, H-B. (2017). Statistical selection and interpretation of imagery features for computer vision-based pavement crack–detection systems. Journal of Performance of Constructed Facilities, 31(5): 04017054. [CrossRef] [Google Scholar]
- Talab, A.M.A., Huang, Z., Xi, F., and HaiMing, L. (2016). Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik-International Journal for Light and Electron Optics 127(3): 1030–1033. [CrossRef] [Google Scholar]
- Hoang, N-D., Nguyen, Q-L., and Bui, D.T., (2018). Image processing–based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony. Journal of Computing in Civil Engineering, 32(5): 04018037. [CrossRef] [Google Scholar]
- Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66. [Google Scholar]
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