[1] A. Kumar, "Computer-vision-based fabric defect detection: A survey," IEEE transactions on industrial electronics, Vol. 55, pp. 348-363, 2008.

[2] A. Kumar and G. K. Pang, "Defect detection in textured materials using Gabor filters," IEEE Transactions on industry applications, Vol. 38, pp. 425-440, 2002.

[3] D. A. Wheeler, B. Brykczynski, and R. N. Meeson Jr, Software Inspection: An Industry Best Practice for Defect Detection and Removal: IEEE Computer Society Press, 1996.

[4] V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM computing surveys (CSUR), Vol. 41, p. 15, 2009.

[5] P. Navarro, C. Fernández-Isla, P. Alcover, and J. Suardíaz, "Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level," Sensors, Vol. 16, p. 1178, 2016.

[6] D. Micucci, M. Mobilio, P. Napoletano, and F. Tisato, "Falls as anomalies? An experimental evaluation using smartphone accelerometer data," Journal of Ambient Intelligence and Humanized Computing, Vol. 8, pp. 87-99, 2017.

[7] M. W. Berry and M. Castellanos, "Survey of text mining," Computing Reviews, Vol. 45, p. 548, 2004.

[8] S. Boriah, V. Chandola, and V. Kumar, "Similarity measures for categorical data: A comparative evaluation," in Proceedings of the 2008 SIAM international conference on data mining, 2008, pp. 243-254.

[9] V. Chandola, S. Boriah, and V. Kumar, "Understanding categorical similarity measures for outlier detection," Technology Report, University of Minnesota, 2008.

[10] M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, "A review of novelty detection," Signal Processing, Vol. 99, pp. 215-249, 2014.

[11] L. Weiwei, Y. Yunhui, L. Jun, Z. Yao, and S. Hongwei, "Automated on-line fast detection for surface defect of steel strip based on multivariate discriminant function," in 2008 Second International Symposium on Intelligent Information Technology Application, 2008, pp. 493-497.

[12] Z. Chao, Z. Desen, and X. Li, "Textural defect detection based on label co-occurrence matrix," JOURNAL-HUAZHONG UNIVERSITY of SCIENCE and TECHNOLOGY NATURE SCIENCE EDITION, Vol. 34, p. 25, 2006.

[13] P. Caleb and M. Steuer, "Classification of surface defects on hot rolled steel using adaptive learning methods," in KES'2000. Fourth International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No. 00TH8516), 2000, pp. 103-108.

[14] K.-L. Mak, P. Peng, and K. F. C. Yiu, "Fabric defect detection using morphological filters," Image and Vision Computing, Vol. 27, pp. 1585-1592, 2009.

[15] A. Cord, F. Bach, and D. Jeulin, "Texture classification by statistical learning from morphological image processing: application to metallic surfaces," Journal of Microscopy, Vol. 239, pp. 159-166, 2010.

[16] D.-H. Shi, T. Gang, S.-Y. Yang, and Y. Yuan, "Research on segmentation and distribution features of small defects in precision weldments with complex structure," NDT and e International, Vol. 40, pp. 397-404, 2007.

[17] R. Anand and P. Kumar, "Flaw detection in radiographic weldment images using morphological watershed segmentation technique," Ndt and E International, Vol. 42, pp. 2-8, 2009.

[18] S. Zhou, D. Liang, and Y. Wei, "Automatic detection of metal surface defects using multi-angle lighting multivariate image analysis," in 2016 IEEE International Conference on Information and Automation (ICIA), 2016, pp. 1588-1593.

[19] C.-h. Chan and G. K. Pang, "Fabric defect detection by Fourier analysis," IEEE transactions on Industry Applications, Vol. 36, pp. 1267-1276, 2000.

[20] B. Zuo and F. Wang, "Surface cutting defect detection of magnet using Fourier image reconstruction," Computer Engineering and Applications, Vol. 52, pp. 256-260, 2016.

[21] J. L. Raheja, S. Kumar, and A. Chaudhary, "Fabric defect detection based on GLCM and Gabor filter: A comparison," Optik, Vol. 124, pp. 6469-6474, 2013.

[22] D. Choi, Y. J. Jeon, J. P. Yun, S. W. Yun, and S. W. Kim, "An algorithm for detecting seam cracks in steel plates," World Acad Sci Eng Technol, Vol. 6, pp. 1456-1459, 2012.

[23] Y.-J. Jeon, D.-c. Choi, J. P. Yun, C. Park, and S. W. Kim, "Detection of scratch defects on slab surface," in 2011 11th International Conference on Control, Automation and Systems, 2011, pp. 1274-1278.

[24] H. Zheng, B. Jiang, and H. Lu, "An adaptive neural-fuzzy inference system (ANFIS) for detection of bruises on Chinese bayberry (Myrica rubra) based on fractal dimension and RGB intensity color," Journal of food engineering, Vol. 104, pp. 663-667, 2011.

[25] M. Yazdchi, M. Yazdi, and A. G. Mahyari, "Steel surface defect detection using texture segmentation based on multifractal dimension," in 2009 International Conference on Digital Image Processing, 2009, pp. 346-350.

[26] J. Blackledge and D. Dubovitskiy, "A surface inspection machine vision system that includes fractal texture analysis," 2008.

[27] Z. Jiuliang, L. Weiwei, Y. Feng, L. Jun, Z. Yao, and Y. Yunhui, "Research on surface quality evaluation system of steel strip based on computer vision," in 2009 Third International Symposium on Intelligent Information Technology Application, 2009, pp. 32-35.

[28] H. Zheng, L. X. Kong, and S. Nahavandi, "Automatic inspection of metallic surface defects using genetic algorithms," Journal of materials processing technology, Vol. 125, pp. 427-433, 2002.

[29] H. Jia, Y. L. Murphey, J. Shi, and T.-S. Chang, "An intelligent real-time vision system for surface defect detection," in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004, pp. 239-242.

[30] C. Lee, C.-H. Choi, J. Choi, Y. Kim, and S. Choi, "Feature extraction algorithm based on adaptive wavelet packet for surface defect classification," in Proceedings of 3rd IEEE International Conference on Image Processing, 1996, pp. 673-676.

[31] A. Kumar and H. C. Shen, "Texture inspection for defects using neural networks and support vector machines," in Proceedings. International Conference on Image Processing, 2002, pp. III-III.

[32] H. Elbehiery, A. Hefnawy, and M. Elewa, "Surface defects detection for ceramic tiles using image processing and morphological techniques," 2005.

[33] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, Vol. 60, pp. 91-110, 2004.

[34] H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European conference on computer vision, 2006, pp. 404-417.

[35] L. Ding and A. Goshtasby, "On the Canny edge detector," Pattern Recognition, Vol. 34, pp. 721-725, 2001.

[36] C. Cusano, P. Napoletano, and R. Schettini, "Intensity and color descriptors for texture classification," in Image Processing: Machine Vision Applications VI, 2013, p. 866113.

[37] P. Napoletano, "Hand-crafted vs learned descriptors for color texture classification," in International Workshop on Computational Color Imaging, 2017, pp. 259-271.

[38] A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, "CNN features off-the-shelf: an astounding baseline for recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2014, pp. 806-813.

[39] P. Napoletano, "Visual descriptors for content-based retrieval of remote-sensing images," International journal of remote sensing, Vol. 39, pp. 1343-1376, 2018.

[40] S. Bianco, L. Celona, P. Napoletano, and R. Schettini, "On the use of deep learning for blind image quality assessment," Signal, Image and Video Processing, Vol. 12, pp. 355-362, 2018.

[41] C. Cusano, P. Napoletano, and R. Schettini, "Combining multiple features for color texture classification," Journal of Electronic Imaging, Vol. 25, p. 061410, 2016.

[42] A. Bhandare, M. Bhide, P. Gokhale, and R. Chandavarkar, "Applications of convolutional neural networks," International Journal of Computer Science and Information Technologies, Vol. 7, pp. 2206-2215, 2016.

[43] O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, "Convolutional neural networks for speech recognition," IEEE/ACM Transactions on audio, speech, and language processing, Vol. 22, pp. 1533-1545, 2014.

[44] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition, 2009, pp. 248-255.

[45] Y. Zhou, H. Nejati, T.-T. Do, N.-M. Cheung, and L. Cheah, "Image-based vehicle analysis using deep neural network: A systematic study," in 2016 IEEE International Conference on Digital Signal Processing (DSP), 2016, pp. 276-280.

[46] J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, "Beyond short snippets: Deep networks for video classification," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 4694-4702.

[47] S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and intelligent laboratory systems, Vol. 2, pp. 37-52, 1987.

[48] E. W. Forgy, "Cluster analysis of multivariate data: efficiency versus interpretability of classifications," biometrics, Vol. 21, pp. 768-769, 1965.

[49] Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on pattern analysis and machine intelligence, Vol. 22, 2000.

[50] J. Heikkila and O. Silven, "A four-step camera calibration procedure with implicit image correction," in cvpr, 1997, p. 1106.

[51] K. Homik, "Approximation capabilities of multilayer feedforward networks," Neural Networks, Vol. 4, pp. 251-257, 1991.

[52] Torkzadeh V, Toosizadeh S. Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces. Measurement and Control. 2019 Sep; 52(7-8):804-13.

[53] Fallahzadeh, M., Farokhi, F., Harimi, A., Sabbaghi-Nadooshan, R. (2021). Facial Expression Recognition based on Image Gradient and Deep Convolutional Neural Network. Journal of AI and Data Mining, 9(2), 259-268. doi: 10.22044/jadm.2021.9898.2121

[54] Erfani, S. (2021). Automatic Facial Expression Recognition Method Using Deep Convolutional Neural Network. Journal of AI and Data Mining, 9(2), 153-159. doi: 10.22044/jadm.2020.8801.2018