مقایسه تعداد لایه‌های تجزیه موجک تصویر به منظور کلاس‌بندی تمام‌خودکار بافت خرابی‌های روسازی آسفالتی

Arabi P, Joshi G, Deepa V, “Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis”, Perspectives in Science, 2016, 8, 203-206. https://doi.org/10.1016/j.pisc.2016.03.018

Armi L, Ershad S, “Texture image analysis and texture classification methods-a review”, International Online Journal of Image Processing and Pattern Recognition, 2019, 2 (1), 1-29. https://doi.org/10.48550/arXiv.1904.06554

Bordiuness A, Khabiri, MM, “Field and laboratory study of the effect of type of emulsion bitumen and chip seal aggregate size applied on roller concrete pavement”, Journal of Civil and Environmental Engineering, 2021, 51 (102), 35-46. https://doi.org/10.22034/jcee.2020.10783

Celic T, Tjahjadi T, “Texture classification and retrieval based on complex wavelet subbands”, Computer and Information Sciences, 2011, 62, 259-264. https://doi.org/10.1007/978-90-481-9794-1_50

Chandra Prabha K, “Texture analysis using glcm & glrlm feature extraction methods”, International Journal for Research in Applied Science and Engineering Technology, 2019, 7 (11), 2059-2064. https://doi.org/10.22214/ijraset.2019.5344

Cheng HD, Glazier C, Hu YG, “Novel approach to pavement cracking detection based on fuzzy set theory”, Journal of Computing in Civil Engineering, 1999, 13 (3), 270-280. https://doi.org/10.1061/(ASCE)08873801(1999)13:4(270)

Chua KM, Xu L, “Simple procedure for identifying pavment distresses from video images”, Journal of Traportation Engineering, 1994, 120 (3), 412-431. https://doi.org/10.1061/(ASCE)0733947X(1994)120:3(412)

Lee D, “A robust position invariant artificial neural network for digital Pavement crack analysis”, Technical report, TRB Annual Meeting, 2003, Washington, DC, USA.

Moghadas Nejad F, Zakeri H, “A comparison of multi-resolution methods for detection and isolation of pavement distress”, Expert Systems with Applications, 2011, 38 (3), 2857-2872. https://doi.org/10.1016/j.eswa.2010.08.079

Moghadas Nejad F, Zakeri H, “An expert system based on wavelet transform and radon neural network for pavement distress classification”, Expert Systems with Applications, 2011, 38 (3), 7088-7101. https://doi.org/10.1016/j.eswa.2010.12.060

Moghadas Nejad F, Zakeri H, “An optimum feature extraction method based on Wavelet-Radon Transform and Dynamic Neural Network for pavement distress classification”, Expert Systems with Applications, 2011, 38 (3), 9442-9460. https://doi.org/10.1016/j.eswa.2011.01.089

Nallamothu S, Wang KCP, “Experimenting with recognition accelerator for pavement distress identification”, Transportation Research Record, 1996, 1536, 130-135.

Nourani V, Ranjbar S, Tootoonchi F, “Change detection of hydrological processes using wavelet-entropy complexity measure case study: Urmia Lake”, Journal of Civil and Environmental Engineering, 2015, 45 (80), 75-86.

Ramola A, Shakya A, Pham D, “Study of statistical methods for texture analysis and their modern evolutions”, Engineering Reports, 2020, 2, 203-206. https://doi.org/10.1002/eng2.12149

Reis M, Bauer A, “Using wavelet texture analysis in image-based classification and statistical process control of paper surface quality”, Computer Aided Chemical Engineering, 2009, 27, 1209-1214. https://doi.org/10.1016/S1570-7946(09)70592-9

Rosa P, “Automatic pavement crack detection and classification system”, Transportation Research Board, National Research Council, Washington DC, 2014, 57-65.

Selvaraj A, Ganesan L, “Texture classification using wavelet transform”, Pattern Recognition Letters, 2003, 24 (9), 1513-1521. https://doi.org/10.1016/S01678655(02)00390-2

Shahabian Moghaddam R, Sahaf A, “Automatic recognition and classification of asphalt pavement distress texture based on wavelet transform”, Road, 2021, 29 (107), 175-197. https://doi.org/10.22034/road.2021.64977.

Shahabian Moghaddam R, Sahaf A, Mohamadzadeh Moghaddam A, Pourreza HR, “A comparison of image texture analysis methods for automatic recognition and classification of asphalt pavement distresses”, Journal of Transportation Infrastructure Engineering, 2017, 3 (3), 1-22. https://doi.org/10.22075/jtie.2017.11322.1211.

Wang KCP, “Wavelet-based pavement distress image edge detection with Trous algorithm”, Transportation Research Record: Journal of the Transportation Research Board, 2009, 2024, 73-81. https://doi.org/10.3141/2024-09

Wang KCP, Li Q, J Yang G, Zhan Y, Qiu Y, “Network level pavement evaluation with 1 mm 3D survey system”, Journal of Traffic and Transportation Engineering, 2015, 2 (6), 391-398. https://doi.org/10.1016/j.jtte.2015.10.005

Zakeri H, Moghadas Nejad F, Fahimifar A, “Image based techniques for crack detection, classification and quantification in asphalt pavement: a review”, Archives of Computational Methods in Engineering, 2016, 24 (4), 1-43. https://doi.org/10.1007/s11831-016-9194-z

Zou Q, Cao Y, Li Q, Mao Q, Wang S, “Crack-tree: automatic crack detection from pavement images”, Pattern Recognition Letters, 2012, 33 (3), 227-238. https://doi.org/10.1016/j.patrec.2011.11.004