MINERÍA la mejor puerta de acceso al sector minero MINERÍA / MAYO 2023 / EDICIÓN 548 53 [28]Loizou CP, Murray V, Pattichis MS, Pantziaris M, Nicolaides AN, Pattichis CS. Despeckle filtering for multiscale amplitude-modulation frequency-modulation (AM-FM) texture analysis of ultrasound images of the intimamedia complex. Int J Biomed Imaging 2014;2014:1–13. [29]Ibraheem NA, Hasan M, Khan R, Mishra PK. Understanding color models: a review. ARPN J Sci Technol 2012;2(3):265–75. [30]Yu HL, MacGregor JF. Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemom Intell Lab Syst 2003;67(2):125–44. [31]Edward JJ. A User’s Guide to Principal Components, Vol. 587. John Wiley & Sons; 2005. [32]Jolliffe I. Principal Component Analysis. Encyclopedia of Statistics in Behavioral Science. 2005. [33]Ilin A, Raiko T. Practical approaches to principal component analysis in the presence of missing values. J Mach Learn Res 2010;11:1957–2000. [34]Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans Ser A Math Phys Eng Sci 2016;374 (2065):20150202. [35]Gonzalez RC, Woods RE. Digital Image Processing. (2nd Edition). Pearson; 2017. [36]Davies ER. Introduction to texture Analysis. In: Handbook of Texture Analysis. Imperial College Press; 2008. p.1-32. [37]Strang G. Wavelets and dilation equations: A brief introduction. SIAM Rev 1989;31(4):614–27. [38]Moulin P. Multiscale image decomposition and wavelets. In: The essential guide to image processing. Academic Press; 2009. [39]Sun ZH, Miller R, Bebis G, Dimeo D. A real-time pre-crash vehicle detection system. In: Proceddings of the sixth IEEE workshop on applications of computer vision. Orlando: IEEE; 2003. [40]Laine A, Fan J. Texture classification by wavelet packet signatures. IEEE Trans Pattern Anal Mach Intell 1993;15(11):1186–91. [41]Bishop CM. Neural networks. In: In: Pattern recognition and machine learning. Springer; 2006. p. 225–90. [42]Murphy KP. Machine learning: A probabilistic perspective. Cambridge, M Ass: MIT Press; 2012. [43]LeCun Y, Bottou L, Orr GB, Müller KR. Efficient BackProp. Neural Networks: Tricks of the Trade. Berlin, Heidelberg: Springer, 2012:9–48. [44]Zhang J, Mani I. KNN approach to unbalanced data distributions: A case study involving information extraction. Proc ICML’2003 Work Learn From Imbalanced Datasets 2003: pp. 1–7. [45]Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014:arXiv: 1412.6980. [46]Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15(1):1929– 58. [47]Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 2020;21(1):6. [48]Rosebrock A. Deep Learning for Computer Vision with Python (1st Ed., Vol.1). Starter Bundle, PyImageSearch, 2017. [49]Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: An overview and application in radiology. Insights Imaging 2018;9(4):611–29. [50]Liu Y, Zhang ZL, Liu X, Wang L, Xia XH. Deep learning-based image classification for online multi-coal and multi-class sorting. Comput Geosci 2021;157:104922. [51]Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014:arXiv: 1409.1556. [52]Patterson DA, Hennessy JL. Computer Organization and Design (ARM Edition): The Hardware-Software Interface. Morgan Kaufmann; 2016. [53]Basler AG. Basler product documentation: daa2500-14uc; 2022.
RkJQdWJsaXNoZXIy MTM0Mzk2