-شهابیان مقدم، ر.، (1396)، " تشخیص و طبقهبندی خودکار خرابیهای روسازی آسفالتی بر پایه آنالیز بافت تصویر در حوزه مکان و تبدیل"، پایاننامه کارشناسی ارشد، اساتید راهنما: سیدعلی صحاف و ابوالفضل محمدزاده مقدم، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران.
-شهابیان مقدم، ر.، صحاف، س. ع.، محمدزادهمقدم، ا. و پوررضا، ح.ر.، (a1396)، "مقایسه روشهای آنالیز بافت تصویر به منظور شناسایی و طبقه بندی خودکار خرابیهای روسازی آسفالتی" ، فصلنامه مهندسی زیر ساختهای حمل و نقل، دوره سوم، شماره سوم، ص. 1-22.
-شهابیان مقدم، ر. صحاف، سس.ع.، محمدزاده مقدم، ا. و پوررضا، ح.ر.، (b1396)، " تشخیص و طبقهبندی خودکار خرابیهای روسازی بر پایه آنالیز بافت تصویر در حوزه مکان و تبدیل"، فصلنامه مهندسی حمل و نقل، دوره نهم، ویژهنامه روسازی، ص. 121-142.
- Aggarawal, N. and Agrawal, R. K. (2012) “First and second order statistics features for classification of magnetic resonance brain images”, Journal of Signal and Information Processing, No. 3, pp. 146-153.
- T. Ahonen, A. Hadid, and M. Pietikäinen, (2006), “Face recognition with Local Binary Patterns: application to face recognition,” IEEE Trans. Pattern Anal. Machine Intel., vol. 28, No. 12, pp. 2037-2041.
- Chang, T. and Kuo, J. (1993) “Texture analysis & classification with tree-Structured wavelet transform”, IEEE Trans. Image Processing, Vol. 2, No. 4, pp. 429-441.
- Cheng, H. D., Glazier, C. and Hu, Y. G. (1999) “Novel approach to pavement cracking detection based on fuzzy set theory”, Journal of Computing in Civil Engineering, Vol. 13, No. 3, pp. 270-280.
- Chua, K. M. and Xu, L. (1994) “Simple procedure for identifying pavement distresses from video images”, Journal of Transportation Engineering, Vol. 120, No. 3, pp. 412-431.
- Dettori, L. and Semlera, L. (2007) “A comparison of wavelet, ridgelet, and curvelet based texture classification algorithms in computed tomography”, Computers in Biology and Medicine, Vol. 37, No. 4, pp. 486-498.
- Gonzalez, R.C. and Woods, R.E. (2006) “Digital image processing 3/E”, Prentice Hall, Upper Saddle River, NJ, USA.
- Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1657–1663, 2010.
- Horng, M.H., Sun, Y.N. and Lin, X.Z. (2000) “Texture feature coding method for classification of liversonography”, Computerized medical imaging & graphics, Vol. 26, pp. 33-42.
- Hoseini Vaez, S., Dehghani, E., Babaei, V. (2017). 'Damage Detection in Post-tensioned Slab Using 2D Wavelet Transforms', Journal of Rehabilitation in Civil Engineering, 5(2), pp. 25-38.
- Kara, B., & Watsuji, N. (2003). Using wavelets for texture classification. In IJCI proceedings of international conference on signal processing, ISN 1304-2386, pp. 920–924.
- Khodakarami, M., Khakpour Moghaddam, H. (2017). 'Evaluating the Performance of Rehabilitated Roadway Base with Geogrid Reinforcement in the Presence of Soil-Geogrid-Interaction', Journal of Rehabilitation in Civil Engineering, 5(1), pp. 33-46.
- Lee, D. (2003) “A robust position invariant artificial neural network for digital Pavement crack analysis”, Technical report, TRB Annual Meeting, 2009, Washington, DC, USA.
- Moghadas Nejad, F. and Zakeri, H., (2011a), “An optimum feature extraction method based on Wavelet–Radon Transform and Dynamic Neural Network for pavement distress classification”, Expert Systems with Applications,Vol. 38, No. 3, pp. 9442-9460.
- Moghadas Nejad, F. and Zakeri, H., (2011b), “A comparison of multi-resolution methods for detection and isolation of pavement distress”, Expert Systems with Applications, Vol. 38, No. 3, pp. 2857-2872.
- Moghadas Nejad, F. and Zakeri, H., (2011c), “An expert system based on wavelet transform and radon neural network for pavement distress classification”, Expert Systems with Applications, Vol. 38, No. 3, pp. 7088-7101.
- Mojsilovic, A. and Sevic, D., (1996), Classification of the ultrasound liver images with the 2N×1D wavelet transform, Proceedings of IEEE Int. Conf. Image Processing, 1, pp. 367-370.
- Nallamothu, S. and Wang, K. C. P., (1996), “Experimenting with recognition accelerator for pavement distress identification”, Transportation Research Record, Vol. 1536, pp. 130-135.
- T. Ojala, M. Pietikäinen, and T. T. Mäenpää, (2002), “Multiresolution grayscale and rotation invariant texture classification with Local Binary Pattern” , IEEE Trans. Pattern Anal. Machine Intell, vol. 24, No. 7,
pp. 971-987.
- Ouyang, A., Dong, Q., Wang, Y. and Liu, Y. (2014) “The classification of pavement crack image based on beamlet algorithm”, in: 7th IFIP WG 5.14 international conference on computer and computing technologies in agriculture, CCTA 2013.
- Singh, R. (2016) “A comparison of gray-level run length matrix and gray-level co-occurrence matrix towards cereal grain classification”, International Journal of Computer Engineering & Technology (IJCET), Vol. 7, No. 6,
pp. 9-17.
- Srinivasan, G. N. and Shobha, G. (2008) “Statistical texture analysis”, proceedings of world academy of science, engineering and technology, No. 36, pp. 207-213.
- Stollnitz, E., DeRose, T., & Salesin, D., (1995), “Wavelets for computer graphics: A primer part 1”, IEEE Computer Graphics and Applications, 15(3), pp.76–84.
- Wang, K. C. P., Li, Q. J., Yang, G., Zhan, Y. and Qiu, Y. (2015) “Network level pavement evaluation with 1 mm 3D survey system”, journal of traffic and transportation engineering, Vol. 2, No. 6, pp. 391-398.
- Wang, K. C. P., (2009), “Wavelet-based pavement distress image edge detection with Trous algorithm”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2024, pp.73-81.
- Wimmer, G., Tamaki, T., Hafner, M., Yoshida, S., Tanaka, S. and Uhl, A., (2016), “Directional wavelet based features for colonic polyp classification”, Medical Image Analysis, Vol. 31, pp. 16-36.
- Zakeri, H., Moghadas Nejad, F. and Fahimifar, A., (2016), “Image based techniques for crack detection, classification and quantification in asphalt pavement: a review”, Archives of Computational Methods in Engineering,
pp. 1-43.
- Zou, Q., Cao, Y., Li, Q., Mao, Q. and Wang, S., (2008), “Cracktree: automatic crack detection from pavement images”, Pattern Recognition Letters, Vol. 33, No. 3,
pp. 227–238.