A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

Referencias bibliográficas

• Li, (2017), Front. Inf. Technol. Electron. Eng., 18, pp. 86, 10.1631/FITEE.1601885
• Zhou, (2018), Engineering, 4, pp. 11, 10.1016/j.eng.2018.01.002
• Zhong, (2017), Engineering, 3, pp. 616, 10.1016/J.ENG.2017.05.015
• Park, (2007), pp. 41
• Espinosa, (2012), J. Mech. Sci. Technol., 26, pp. 545, 10.1007/s12206-011-1214-1
• LeCun, (2015), Nature, 521, pp. 436, 10.1038/nature14539
• DeCost, (2015), Comput. Mater. Sci., 110, pp. 126, 10.1016/j.commatsci.2015.08.011
• Chowdhury, (2016), Comput. Mater. Sci., 123, pp. 176, 10.1016/j.commatsci.2016.05.034
• Holm, (2020), Metall. Mater. Trans. A, pp. 1
• Szeliski, (2010)
• Brice, (1970), Artificial Intelligence, 1, pp. 205, 10.1016/0004-3702(70)90008-1
• Pavlidis, (1977)
• Riseman, (1977), Comput. Graph. Image Process., 6, pp. 221, 10.1016/S0146-664X(77)80028-2
• Pham, (2000), Annu. Rev. Biomed. Eng., 2, pp. 315, 10.1146/annurev.bioeng.2.1.315
• Hesamian, (2019), J. Digit. Imaging, 32, pp. 582, 10.1007/s10278-019-00227-x
• Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele, The cityscapes dataset for semantic urban scene understanding, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3213–3223.
• Horn, (1986)
• Minaee, (2021), IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2021.3059968
• Hao, (2020), Neurocomputing, 406, pp. 302, 10.1016/j.neucom.2019.11.118
• Lateef, (2019), Neurocomputing, 338, pp. 321, 10.1016/j.neucom.2019.02.003
• Garcia-Garcia, (2018), Appl. Soft Comput., 70, pp. 41, 10.1016/j.asoc.2018.05.018
• Rosenthal, (2014), Metallogr. Microstruct. Anal., 3, pp. 448, 10.1007/s13632-014-0168-y
• Morales-Hernández, (2010), J. Mater Process. Technol., 210, pp. 335, 10.1016/j.jmatprotec.2009.09.020
• Kurzydlowski, (1995)
• Hochrainer, (2007), Phil. Mag., 87, pp. 1261, 10.1080/14786430600930218
• Rasband, (1997)
• Li, (2020), Signal Image Video Process., pp. 1
• Fukushima, (1982), pp. 267
• LeCun, (1998), Proc. IEEE, 86, pp. 2278, 10.1109/5.726791
• Ciregan, (2012), pp. 3642
• Azimi, (2018), Sci. Rep., 8, pp. 1, 10.1038/s41598-018-20037-5
• Jang, (2020), Sci. Technol. Weld. Join., 25, pp. 282, 10.1080/13621718.2019.1687635
• Vander Voort, (2004)
• Li, (2018), Mater. Charact., 142, pp. 179, 10.1016/j.matchar.2018.05.038
• Vander Voort, (2004), Metallogr. Microstruct., pp. 493, 10.31399/asm.hb.v09.a0003752
• Michael Kampffmeyer, Arnt-Borre Salberg, Robert Jenssen, Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016, pp. 1–9.
• Hausen, (2020), Astrophys. J. Suppl. Ser., 248, pp. 1, 10.3847/1538-4365/ab8868
• Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, Antonio Torralba, Scene parsing through ADE20K dataset, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
• Cordts, (2015)
• Zhang, (2019), J. Electron. Imaging, 28, 10.1117/1.JEI.28.3.033035
• DeCost, (2017), Integr. Mater. Manuf. Innov., 6
• Roberts, (2019), Sci. Rep., 9, pp. 1, 10.1038/s41598-019-49105-0
• Chen, (2021), IEEE Access, 9, pp. 30858, 10.1109/ACCESS.2021.3059505
• Cui, (2020), Appl. Sci., 10, pp. 545, 10.3390/app10020545
• DeCost, (2019), Microsc. Microanal., 25, pp. 21, 10.1017/S1431927618015635
• Asako Kanezaki, Unsupervised image segmentation by backpropagation, in: Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, 2018, pp. 1543–1547.
• Kim, (2020), Sci. Rep., 10, pp. 1
• Lievers, (2004), Mater. Sci. Eng. A, 381, pp. 134, 10.1016/j.msea.2004.04.002
• Kim, (2019), Microsc. Microanal., 25, pp. 1139, 10.1017/S1431927619014752
• Li Chen, Min Jiang, JianXun Chen, Image segmentation using iterative watersheding plus ridge detection, in: Proceedings of the 16th IEEE International Conference on Image Processing, 2009, pp. 4033–4036.
• Jørgensen, (2010), Ultramicroscopy, 110, pp. 216, 10.1016/j.ultramic.2009.11.013
• de Albuquerque, (2009), NDT & E Int., 42, pp. 644, 10.1016/j.ndteint.2009.05.002
• Chen, (2020), Symmetry, 12, pp. 639, 10.3390/sym12040639
• Ma, (2018), Symmetry, 10, pp. 107, 10.3390/sym10040107
• Chen, (2019), J. Electron. Imaging, 28, pp. 1
• Ali, (2021), J. Synchrotron Radiat., 28, 10.1107/S1600577521001314
• Bulgarevich, (2018), Sci. Rep., 8, 10.1038/s41598-018-20438-6
• Papa, (2013), Expert Syst. Appl., 40, pp. 590, 10.1016/j.eswa.2012.07.062
• Ronneberger, (2015), pp. 234
• Zhou, (2018), pp. 3
• Chaurasia, (2017), pp. 1
• Bansal, (2017)
• Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár, Panoptic feature pyramid networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 6399–6408.
• Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, Pyramid scene parsing network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2881–2890.
• Li, (2018)
• Chen, (2017)
• Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European Conference on Computer Vision, 2018, pp. 801–818.
• Selim Seferbekov, Vladimir Iglovikov, Alexander Buslaev, Alexey Shvets, Feature pyramid network for multi-class land segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 272–275.
• Tarun Kalluri, Girish Varma, Manmohan Chandraker, CV Jawahar, Universal semi-supervised semantic segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 5259–5270.
• Han, (2019), J. Electron. Imaging, 28, 10.1117/1.JEI.28.1.013030
• Chen, (2015), pp. 519
• Chen, (2020), Int. J. Appl. Electromagn. Mech., pp. 1
• Sezgin, (2004), J. Electron. Imaging, 13, pp. 146, 10.1117/1.1631315
• Sahoo, (1988), Comput. Vis. Graph. Image Process., 41, pp. 233, 10.1016/0734-189X(88)90022-9
• Liao, (2001), J. Inf. Sci. Eng., 17, pp. 713
• Sarkar, (2013), pp. 1
• Ghamisi, (2012), Expert Syst. Appl., 39, pp. 12407, 10.1016/j.eswa.2012.04.078
• Oliva, (2013), J. Appl. Math., 2013, 10.1155/2013/575414
• Li, (1993), Pattern Recognit., 26, pp. 617, 10.1016/0031-3203(93)90115-D
• Otsu, (1979), IEEE Trans. Syst. Man Cybern., 9, pp. 62, 10.1109/TSMC.1979.4310076
• Yen, (1995), IEEE Trans. Image Process., 4, pp. 370, 10.1109/83.366472
• Kittler, (1986), Pattern Recognit., 19, pp. 41, 10.1016/0031-3203(86)90030-0
• Ridler, (1978), IEEE Trans. Syst. Man Cybern., 8, pp. 630, 10.1109/TSMC.1978.4310039
• Tsai, (1985), Comput. Vis. Graph. Image Process., 29, pp. 377, 10.1016/0734-189X(85)90133-1
• Kapur, (1985), Comput. Vis. Graph. Image Process., 29, pp. 273, 10.1016/0734-189X(85)90125-2
• Sahoo, (1997), Pattern Recognit., 30, pp. 71, 10.1016/S0031-3203(96)00065-9
• Abutaleb, (1989), Comput. Vis. Graph. Image Process., 47, pp. 22, 10.1016/0734-189X(89)90051-0
• Wong, (1989), IEEE Trans. Syst. Man Cybern., 19, pp. 866, 10.1109/21.35351
• Pal, (1989), Signal Process., 16, pp. 97, 10.1016/0165-1684(89)90090-X
• Adams, (1994), IEEE Trans. Pattern Anal. Mach. Intell., 16, pp. 641, 10.1109/34.295913
• Tremeau, (1997), Pattern Recognit., 30, pp. 1191, 10.1016/S0031-3203(96)00147-1
• Meyer, (1994), Signal Process., 38, pp. 113, 10.1016/0165-1684(94)90060-4
• Gauch, (1993), IEEE Trans. Pattern Anal. Mach. Intell., 15, pp. 635, 10.1109/34.216734
• Terzopoulos, (1988), Vis. Comput., 4, pp. 306, 10.1007/BF01908877
• Michael Kass, Andrew Witkin, Demetri Terzopoulos, Snakes: Active contour models, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, 1988, pp. 321–331.
• Osher, (2006)
• Felzenszwalb, (2004), Int. J. Comput. Vis., 59, pp. 167, 10.1023/B:VISI.0000022288.19776.77
• Nameirakpam, (2015), Procedia Comput. Sci., 54, pp. 764, 10.1016/j.procs.2015.06.090
• Cheng, (1995), IEEE Trans. Pattern Anal. Mach. Intell., 17, pp. 790, 10.1109/34.400568
• Jin, (2010), pp. 563
• Ester, (1996), pp. 226
• Kumar, (2020), Int. J. Innov. Technol. Explor. Eng., 8
• Achanta, (2010)
• Bezdek, (1984), Comput. Geosci., 10, pp. 191, 10.1016/0098-3004(84)90020-7
• Kohonen, (1990), Proc. IEEE, 78, pp. 1464, 10.1109/5.58325
• Liu, (2009), pp. 344
• Zhu, (2009), Synth. Lect. Artif. Intell. Mach. Learn., 3, pp. 1
• Jing, (2020), IEEE Trans. Pattern Anal. Mach. Intell.
• Fenga, (2020)
• Grandvalet, (2005), pp. 281
• Nasim Souly, Concetto Spampinato, Mubarak Shah, Semi supervised semantic segmentation using generative adversarial network, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 5688–5696.
• Hung, (2018)
• Simonyan, (2015)
• He, (2015)
• Tan, (2019), pp. 6105
• Jégou, (2017), pp. 1175
• Long, (2015), pp. 3431
• Dietterich, (2000), pp. 1
• Wolpert, (1992), Neural Netw., 5, pp. 241, 10.1016/S0893-6080(05)80023-1
• Sun, (2019)
• Shorten, (2019), J. Big Data, 6, pp. 1, 10.1186/s40537-019-0197-0
• Izmailov, (2018)
• Athiwaratkun, (2018)
• Jadon, (2020), pp. 1
• Kim, (2020), IEEE Trans. Image Process., 29, pp. 8055, 10.1109/TIP.2020.3011269
• Bradski, (2000), Dr. Dobb’s J. Softw. Tools
• Van der Walt, (2014), PeerJ, 2
• Fisher Yu, Vladlen Koltun, Thomas Funkhouser, Dilated residual networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 472–480.
• Yakubovskiy, (2020)
• Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama, Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
• Deng, (2009), pp. 248