Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado

graficas, tablas

Autores:: Villegas Jaramillo, Eduardo José

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
dc.title.translated.eng.fl_str_mv	Heterogeneous methodology for automatic visual inspection based on inexactly supervised learning techniques
title	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
spellingShingle	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado 670 - Manufactura::679 -Otros productos de materiales específicos Inspección visual automática Aprendizaje de múltiples instancias Descomposición en bloques Extracción de características Interpretación gráfica Supervisión inexacta Disimilitudes Localización de defectos Automatic visual inspection Multiple instance learning Block decomposition Feature extraction Graphical interpretation Inexact supervision Dissimilarities Defect localization
title_short	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
title_full	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
title_fullStr	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
title_full_unstemmed	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
title_sort	Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado
dc.creator.fl_str_mv	Villegas Jaramillo, Eduardo José
dc.contributor.advisor.none.fl_str_mv	Orozco-Alzate, Mauricio
dc.contributor.author.none.fl_str_mv	Villegas Jaramillo, Eduardo José
dc.contributor.researchgroup.spa.fl_str_mv	Gaia Grupo de Ambientes Inteligentes Adaptativos
dc.contributor.orcid.spa.fl_str_mv	Villegas Jaramillo, Eduardo José [https://orcid.org/0000-0002-7563-2913]
dc.contributor.cvlac.spa.fl_str_mv	Villegas Jaramillo, Eduardo José [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000625698]
dc.subject.ddc.spa.fl_str_mv	670 - Manufactura::679 -Otros productos de materiales específicos
topic	670 - Manufactura::679 -Otros productos de materiales específicos Inspección visual automática Aprendizaje de múltiples instancias Descomposición en bloques Extracción de características Interpretación gráfica Supervisión inexacta Disimilitudes Localización de defectos Automatic visual inspection Multiple instance learning Block decomposition Feature extraction Graphical interpretation Inexact supervision Dissimilarities Defect localization
dc.subject.proposal.spa.fl_str_mv	Inspección visual automática Aprendizaje de múltiples instancias Descomposición en bloques Extracción de características Interpretación gráfica Supervisión inexacta Disimilitudes Localización de defectos
dc.subject.proposal.eng.fl_str_mv	Automatic visual inspection Multiple instance learning Block decomposition Feature extraction Graphical interpretation Inexact supervision Dissimilarities Defect localization
description	graficas, tablas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-05-17T21:31:13Z
dc.date.available.none.fl_str_mv	2024-05-17T21:31:13Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86112
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86112 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Aliyu Abubakar, Mohammed Ajuji, and Ibrahim Usman Yahya. Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination. Applied System Innovation, 3(2), 2020. ISSN 2571-5577. doi: 10.3390/asi3020020. Ethem Alpaydin, Veronika Cheplygina, Marco Loog, and David M.J. Tax. Single- vs. multiple-instance classification. Pattern Recognition, 48(9):2831--2838, 2015. ISSN 00313203. doi: 10.1016/j.patcog.2015.04.006. Jaume Amores. Multiple instance classification: Review, taxonomy and comparative study. Artificial Intelligence, 201:81--105, 2013. ISSN 0004-3702. doi: 10.1016/j.artint.2013.06.003. Katharina Anding, Lilli Haar, Galina Polte, Jurij Walz, and Gunther Notni. Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks. In Proceedings Volume 11144, Photonics and Education in Measurement Science 2019, page 26, 09 2019. doi: 10.1117/12.2530899. Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. Support Vector Machines for Multiple-Instance Learning. Advances in Neural Information Processing Systems, 15:561--568, 01 2002. Massimo Aria and Corrado Cuccurullo. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4):959--975, 2017. doi: 10.1016/j.joi.2017.08.007. Boris Babenko. Multiple Instance Learning: Algorithms and Applications. In Computer Science, Mathematics, 01 2008. URL https://api.semanticscholar.org/CorpusID:2153770. Saber Mirzaee Bafti, Chee Siang Ang, Md Moinul Hossain, Gianluca Marcelli, Marc Alemany-Fornes, and Anastasios D. Tsaousis. A crowdsourcing semi-automatic image segmentation platform for cell biology. Computers in Biology and Medicine, 130:104204, 2021. ISSN 0010-4825. doi: 10.1016/j.compbiomed.2020.104204. Ganbayar Batchuluun, Jiho Choi, and Kang Ryoung Park. CAM-CAN: Class activation map-based categorical adversarial network. Expert Systems with Applications, 222:119809, 2023. ISSN 0957-4174. doi: 10.1016/j.eswa.2023.119809. Laura Lucía Becerra Elejalde. Supercoco se renueva tras 70 años e incursiona con agua de coco en su portafolio. La República, June 16th, 2020. https://tinyurl.com/43dsd9ad. Samia Benyahia, Boudjelal Meftah, and Olivier Lézoray. Multi-features extraction based on deep learning for skin lesion classification. Tissue and Cell, 74:101701, 2022. ISSN 0040-8166. doi: 10.1016/j.tice.2021.101701. Jeff Bier. Is deep learning the solution to all computer vision problems? Blog post at the Vision Systems Design website, 2019. URL https://tinyurl.com/bdf8yfw2 Tiago Botari, Rafael Izbicki, and Andre C. P. L. F. de Carvalho. Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. In Peggy Cellier and Kurt Driessens, editors, Machine Learning and Knowledge Discovery in Databases, pages 241--252, Cham, 2020. Springer International Publishing. ISBN 978-3-030-43823-4. Jakob Božič, Domen Tabernik, and Danijel Skočaj. End-to-end training of a two-stage neural network for defect detection. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 5619--5626, 2021. doi: 10.1109/icpr48806.2021.9412092. Jakob Božič, Domen Tabernik, and Danijel Skočaj. Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Computers in Industry, 129:103459, 08 2021. doi: 10.1016/j.compind.2021.103459. Max Bramer. Principles of Data Mining. Springer, fourth edition edition, 2020. ISBN 978-1-4471-7493-6. doi: 10.1007/978-1-4471-7493-6. Gilles Brassard and Paul Bratley. Fundamentos de Algoritmia. Prentice Hall, Montreal, primera edition, 1997. ISBN 84-89660-00-X. Marc André Carbonneau, Veronika Cheplygina, Eric Granger, and Ghyslain Gagnon. Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognition, 77:329--353, 2017. ISSN 00313203. doi: 10.1016/j.patcog.2017.10.009. Ssu-Han Chen and Der-Baau Perng. Directional textures auto-inspection using principal component analysis. International Journal of Advanced Manufacturing Technology, 55:1099--1110, 08 2011. doi: 10.1007/s00170-010-3141-1. Wen-Chin Chen and Shou-Wen Hsu. A neural-network approach for an automatic LED inspection system. Expert Systems with Applications, 33:531--537, 08 2007. doi: 10.1016/j.eswa.2006.06.011. Min-Yuan Cheng, Riqi Radian Khasani, and Kent Setiono. Image quality enhancement using HybridGAN for automated railway track defect recognition. Automation in Construction, 146:104669, 2023. ISSN 0926-5805. doi: 10.1016/j.autcon.2022.104669. Veronika Cheplygina and David M. J. Tax. Characterizing Multiple Instance Datasets. In Similarity-Based Pattern Recognition, pages 15--27, Cham, 2015. Springer International Publishing. ISBN 978-3-319-24261-3. doi: 10.1007/978-3-319-24261-3_2. Veronika Cheplygina, David M.J. Tax, and Marco Loog. Multiple instance learning with bag dissimilarities. Pattern Recognition, 48 (1):264--275, 2015. ISSN 0031-3203. doi: 10.1016/j.patcog.2014.07.022. Veronika Cheplygina, David M. J. Tax, and Marco Loog. Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Transactions on Neural Networks and Learning Systems, 27(6):1379--1391, June 2016. ISSN 2162-237X. doi: 10.1109/TNNLS.2015. 2424254. François Chollet. Deep Learning with Python. Manning Publications, 2 edition, October 2021. ISBN 9781617296864. URL https://www.manning.com/books/deep-learning-with-python-second-edition. Roger Chugh, Vardaan Bhatia, Karan Khanna, and Vandana Bhatia. A Comparative Analysis of Classifiers for Image Classification. In 2020 - 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 248--253, 01 2020. doi: 10.1109/Confluence47617.2020.9058042. Yandre Costa, Diego Bertolini, Alceu Jr, George Cavalcanti, and Luiz Soares de Oliveira. The dissimilarity approach: a review. Artificial Intelligence Review, 53:2783--2808, 04 2020. doi: 10.1007/s10462-019-09746-z. Esteban Cumbajin, Nuno Rodrigues, Paulo Costa, Rolando Miragaia, Luís Frazão, Nuno Costa, Antonio Fernández-Caballero, Jorge Carneiro, Leire H. Buruberri, and António Pereira. A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection. Journal of Imaging, 9(10), 2023. ISSN 2313-433X. doi: 10.3390/jimaging9100193. Suresh Dara and Priyanka Tumma. Feature Extraction By Using Deep Learning: A Survey. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 1795--1801, 2018. doi: 10.1109/ICECA.2018.8474912. Shubhabrata Datta and J. Paulo Davim. Machine Learning in Industry. Management and Industrial Engineering. Springer, Cham, Switzerland, 2022. ISBN 978-3-030-75846-2. doi: 10.1007/978-3-030-75847-9. Soham De, Anirbit Mukherjee, and Enayat Ullah. Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration. arXiv, 2018. doi: 10.48550/ARXIV.1807.06766. Deltamax Automazione . Electronic References, 2016. URL http://www.deltamaxautomazione.it/risolvi/. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248--255, 2009. doi: 10.1109/CVPR.2009.5206848. Thomas G. Dietterich, Richard H. Lathrop, and Tomás Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89:31--71, 1997. ISSN 0004-3702/97. Peilei Dong and Wenmin Wang. Better region proposals for pedestrian detection with R-CNN. In 2016 Visual Communications and Image Processing (VCIP), pages 1--4, 2016. doi: 10.1109/VCIP.2016.7805452. Robert P W Duin. Dissimilarity measures , Pattern Recognition Tools, 2012. URL http://37steps.com/1264/dissimilarity-measures/. Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, and Marco Loog. Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance. In Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, and Marcello Pelillo, editors, Structural, Syntactic, and Statistical Pattern Recognition, pages 183--192, Berlin, Heidelberg, 2014. Springer Berlin Heidelberg. ISBN 978-3-662-44415-3. doi: 10.1007/978-3-662-44415-3_19. Robert P.W. Duin and Elzbieta Pekalska. The dissimilarity representation for pattern recognition, a tutorial. http://rduin.nl/ presentations/DisRep_Tutorial.pdf, 2011. Ross Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440--1448, 2015. doi: 10.1109/ICCV.2015.169. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 580--587, 2014. doi: 10.1109/CVPR.2014.81. Facundo E. Godoy. Métodos clásicos de clasificación: comparación y aplicación. Trabajo especial licenciatura en matemática, Facultad de Matemática, Astronomía, Física y Computación. Universidad Nacional de Córdoba, July 2021. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org. Luiz G. Hafemann, Luiz S. Oliveira, Paulo R. Cavalin, and Robert Sabourin. Transfer learning between texture classification tasks using Convolutional Neural Networks. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1--7, 2015. doi: 10.1109/IJCNN.2015.7280558. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. In David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, editors, Computer Vision -- ECCV 2014, pages 346--361, Cham, 2014. Springer International Publishing. ISBN 978-3-319-10578-9. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2980--2988, 2017. doi: 10.1109/ICCV.2017.322. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, and Sarah Vluymans. Multiple instance learning: Foundations and algorithms. Springer International Publishing, 2016. ISBN 9783319477596. doi: 10.1007/978-3-319-47759-6. Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, Malcolm Slaney, Ron J. Weiss, and Kevin Wilson. CNN Architectures for Large-Scale Audio Classification, 2017. O. Hmidani and E. M. Ismaili Alaoui. A comprehensive survey of the R-CNN family for object detection. In 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), pages 1--6, 2022. doi: 10.1109/CommNet56067. 2022.9993862. Rudolf Hoffmann and Christoph Reich. A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing. Electronics, 12(22), 2023. ISSN 2079-9292. doi: 10.3390/electronics12224572. Shih-Chung Hsu, Chung-Lin Huang, and Cheng-Hung Chuang. Vehicle detection using simplified fast R-CNN. In 2018 International Workshop on Advanced Image Technology (IWAIT), pages 1--3, 2018. doi: 10.1109/IWAIT.2018.8369767. Shiluo Huang, Zheng Liu, Wei Jin, and Ying Mu. Bag dissimilarity regularized multi-instance learning. Pattern Recognition, 126: 108583, 2022. ISSN 0031-3203. doi: 10.1016/j.patcog.2022.108583. Yibin Huang, Congying Qiu, Xiaonan Wang, Shijun Wang, and Kui Yuan. A Compact Convolutional Neural Network for Surface Defect Inspection. Sensors, 20(7), 2020. ISSN 1424-8220. doi: 10.3390/s20071974. URL https://www.mdpi.com/1424-8220/20/7/1974. Dejice Jacob, Phil Trinder, and Jeremy Singer. Python programmers have GPUs too: automatic Python loop parallelization with staged dependence analysis. In Stefan Marr and Juan Fumero, editors, Proceedings of the 15th ACM SIGPLAN International Symposium on Dynamic Languages, DLS 2019, Athens, Greece, October 20, 2019, pages 42--54. ACM, 2019. ISBN 978-1-4503-6996-1. doi: 10.1145/3359619.3359743. Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip Yu, and Yue Zhao. Weakly Supervised Anomaly Detection: A Survey, 02 2023. Manjunath Jogin, Mohana, M S Madhulika, G D Divya, R K Meghana, and S Apoorva. Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), pages 2319--2323, 2018. doi: 10.1109/RTEICT42901.2018.9012507. M. Keshavarzi, Mohammad Ali Dehghan, and Mashaallah Mashinchi. Classification based on similarity and dissimilarity through equivalence classes. Applied and Computational Mathematics, 8:203--215, 01 2009. Salman Khan, Hossein Rahmani, and Syed Afaq Ali Shah. A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers, 2018. ISBN 1681730219. S. Kornblith, J. Shlens, and Q. V. Le. Do Better ImageNet Models Transfer Better? In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2656--2666, Los Alamitos, CA, USA, jun 2019. IEEE Computer Society. doi: 10.1109/CVPR.2019.00277. Bangjun Lei, Guangzhu Xu, Ming Feng, Yaobin Zou, Ferdinand Van der Heijden, Dick de Ridder, and David M. J. Tax. Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. Wiley, 2017. doi: 10.1002/9781119152484. Christian Leistner, Amir Saffari, and Horst Bischof. MIForests: Multiple-Instance Learning with Randomized Trees. In Kostas Daniilidis, Petros Maragos, and Nikos Paragios, editors, Computer Vision -- ECCV 2010, pages 29--42, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg. ISBN 978-3-642-15567-3. doi: 10.1007/978-3-642-15567-3_3. Li Li, Zuopai Zhou, Na Bai, Tao Wang, Kan-Hao Xue, Huajun Sun, Qiang He, Weiming Cheng, and Xiangshui Miao. Naive Bayes classifier based on memristor nonlinear conductance. Microelectronics Journal, 129:105574, 2022. ISSN 0026-2692. doi: 10.1016/j.mejo.2022.105574. Pengcheng Li, Zihao Dong, Jianjie Shi, Zengzhi Pang, and Jinping Li. Detection of Small Size Defects in Belt Layer of Radial Tire Based on Improved Faster R-CNN. In 2021 11th International Conference on Information Science and Technology (ICIST), pages 531--538, 2021a. doi: 10.1109/ICIST52614.2021.9440580. Shengyuan Li and Xuefeng Zhao. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Advances in Civil Engineering, 2019:1--12, 04 2019. doi: 10.1155/2019/6520620. Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, and Dejing Dou. Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond, 2021b. Yu Li and ZiWei Wang. Research on Textile Defect Detection Based on Improved Cascade R-CNN. In 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), pages 43--46, 2021. doi: 10.1109/AIEA53260.2021.00017. Yu Liang, Siguang Li, Chungang Yan, Maozhen Li, and Changjun Jiang. Explaining the black-box model: A survey of local interpretation methods for deep neural networks. Neurocomputing, 419:168--182, 2021. ISSN 0925-2312. doi: 10.1016/j.neucom.2020.08.011. Hong-Dar Lin and Duan-Cheng Ho. Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems. The International Journal of Advanced Manufacturing Technology, 34:567--583, 09 2007. doi: 10.1007/s00170-006-0614-3. Bo Liu, Zhi-Feng Hao, and Xiao-Wei Yang. Nesting support vector machinte for muti-classification [machinte read machine]. In 2005 International Conference on Machine Learning and Cybernetics, volume 7, pages 4220--4225 Vol. 7, 2005. doi: 10.1109/ICMLC. 2005.1527678. Caihui Liu, Bowen Lin, Jianying Lai, and Duoqian Miao. An improved decision tree algorithm based on variable precision neighborhood similarity. Information Sciences, 615:152--166, 2022a. ISSN 0020-0255. doi: 10.1016/j.ins.2022.10.043. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. SSD: Single Shot MultiBox Detector. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision — ECCV 2016, pages 21--37, Cham, 2016. Springer International Publishing. doi: 10.1007/978-3-319-46448-0_2. Wei Liu, Jiarui Zhang, and Yijun Zhao. A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans. In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pages 998--1001, 2022b. doi: 10.1109/COMPSAC54236.2022.00154. Tanya Makkar, Yogesh Kumar, Ashwani Kr Dubey, Álvaro Rocha, and Ayush Goyal. Analogizing time complexity of KNN and CNN in recognizing handwritten digits. In 2017 Fourth International Conference on Image Information Processing (ICIIP), pages 1--6, 2017. doi: 10.1109/ICIIP.2017.8313707. Elias N. Malamas, Euripides G M Petrakis, Michalis Zervakis, Laurent Petit, and Jean Didier Legat. A survey on industrial vision systems, applications and tools. Image and Vision Computing, 21(2):171--188, 2003. ISSN 0262-8856/03. Borja Marin, Keith Brown, and Mustafa S. Erden. Automated Masonry crack detection with Faster R-CNN. In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pages 333--340, 2021. doi: 10.1109/CASE49439.2021. 9551683. Oded Maron and Aparna Lakshmi Ratan. Multiple-Instance Learning for Natural Scene Classification. In Proceedings of the Fifteenth International Conference on Machine Learning, ICML ’98, page 341–349, San Francisco, CA, USA, 1998. Morgan Kaufmann Publishers Inc. ISBN 1558605568. URL https://dl.acm.org/doi/10.5555/645527.657299. Carlos Mera. Detección de defectos en sistemas de inspección visual automática a través del aprendizaje de múltiples instancias. Tesis doctoral, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia, may 2017. URL https://repositorio.unal. edu.co/handle/unal/59346. Doctorate in Engineering - Systems and Informatics. Carlos Mera, Mauricio Orozco-Alzate, John Branch, and Domingo Mery. Automatic visual inspection: An approach with multi-instance learning. Computers in Industry, 83:46--54, 2016. doi: 10.1016/j.compind.2016.09.002. Carlos Mera, Mauricio Orozco-Alzate, and John Branch. Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection. Computers in Industry, 109:153--164, aug 2019. doi: 10.1016/j.compind.2019.04.006. Domingo Mery. BALU: A Matlab toolbox for computer vision, pattern recognition and image processing, 2011. URL http://dmery.ing. puc.cl/index.php/balu/. Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267:1--38, 2019. ISSN 0004-3702. doi: 10.1016/j.artint.2018.07.007. H. B. Mitchell. Image Similarity Measures, pages 167--185. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. ISBN 978-3-642-11216-4. doi: 10.1007/978-3-642-11216-4_14. Elhassan Mohamed, Konstantinos Sirlantzis, and Gareth Howells. A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation. Displays, 73:102239, 2022. ISSN 0141-9382. doi: 10.1016/j.displa.2022.102239. Divya Pramasani Mohandoss, Yong Shi, and Kun Suo. Outlier Prediction Using Random Forest Classifier. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pages 0027--0033, 2021. doi: 10.1109/CCWC51732.2021. 9376077. Christoph Molnar. Interpretable Machine Learning. Independently published, 2019. URL https://christophm.github.io/ interpretable-ml-book/. Romain Mormont, Pierre Geurts, and Raphaël Marée. Comparison of Deep Transfer Learning Strategies for Digital Pathology. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 2343--2352, 2018. doi: 10.1109/CVPRW.2018.00303. Saad Naeem, Noreen Jamil, Habib Ullah Khan, and Shah Nazir. Complexity of Deep Convolutional Neural Networks in Mobile Computing. Complexity, 2020:3853780, Sep 2020. ISSN 1076-2787. doi: 10.1155/2020/3853780. Tam Nguyen and Raviv Raich. Incomplete Label Multiple Instance Multiple Label Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3):1320--1337, 2022. doi: 10.1109/tpami.2020.3017456. Mark Nixon and Alberto Aguado. Low-level feature extraction (including edge detection), pages 137--216. Photonics and Education in Measurement Science, 12 2012. ISBN 9780123965493. doi: 10.1016/B978-0-12-396549-3.00004-5. C. Guadalupe Origel-Rivas, Eréndira Rendón Lara, Itzel Abundez Barrera, and Roberto Alejo Eleuterio. Redes neuronales artificiales y árboles de decisión para la clasificación con datos categóricos. Res. Comput. Sci., 149(8):541--554, 2020. Taiwo R. Oyedare, Vijay Shah, Daniel Jakubisin, and Jeffrey Reed. Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks. arXiv - EE - Signal Processing, 03 2023. doi: 10.48550/arXiv.2303.03326. Christian Payer, Darko Štern, Horst Bischof, and Martin Urschler. Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Medical Image Analysis, 54:207--219, 2019. ISSN 1361-8415. doi: 10.1016/j.media.2019.03.007. Elzbieta Pekalska and Robert P. W. Duin. The dissimilarity representation for pattern recognition , Foundations and Applications. World Scientific, Delft, 2005. ISBN 9812565302. doi: 10.1142/9789812703170. Jacek Piotrowski. Top-Down Approach to Image Similarity Measures. In Leonard Bolc, Juliusz L. Kulikowski, and Konrad Woj- ciechowski, editors, Computer Vision and Graphics, pages 66--69, Berlin, Heidelberg, 2009. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-02345-3_7. Leonardo Plotegher, Chiara Corridori, Fabio Dolci, Claudio Andreatta, Silvio Benini, and Matteo Devilli. The GI dataset for glass inspection 1st release (August 2016), 2016. URL http://www.deltamaxautomazione.it/risolvi. Diana Porro-Munoz, Robert P W Duin, Isneri Talavera, and Mauricio Orozco-Alzate. Classification of three-way data by the dissimilarity representation. Signal Processing, 91(11):2520--2529, 2011. ISSN 01651684. doi: 10.1016/j.sigpro.2011.05.004. Konpat Preechakul, Sira Sriswasdi, Boonserm Kijsirikul, and Ekapol Chuangsuwanich. Improved image classification explainability with high-accuracy heatmaps. iScience, 25(3):103933, 2022. ISSN 2589-0042. doi: 10.1016/j.isci.2022.103933. Q.R. Razlighi, N. Kehtarnavaz, and S. Yousefi. Evaluating similarity measures for brain image registration. Journal of Visual Communication and Image Representation, 24(7):977--987, 2013. ISSN 1047-3203. doi: 10.1016/j.jvcir.2013.06.010. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779--788, 2016. doi: 10.1109/CVPR.2016.91. Payam Refaeilzadeh, Lei Tang, and Huan Liu. Cross-Validation, pages 532--538. Springer US, Boston, MA, 2009. ISBN 978-0-387- 39940-9. doi: 10.1007/978-0-387-39940-9_565. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(06):1137--1149, jun 2017. ISSN 1939-3539. doi: 10.1109/TPAMI.2016.2577031. Amal Saadallah, Jan Buescher, Omar Abdulaaty, Thorben Panusch, Jochen Deuse, and Katharina Morik. Explainable Predictive Quality Inspection using Deep Learning in Electronics Manufacturing. Procedia CIRP, 107:594--599, 01 2022. doi: 10.1016/j.procir.2022.05.031. Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision, 128(2): 336--359, Feb 2020. ISSN 1573-1405. doi: 10.1007/s11263-019-01228-7. Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, and Yann LeCun. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. International Conference on Learning Representations (ICLR) Banff, 2013. doi: 10.48550/ARXIV.1312.6229. Bhoomi Shah and Hetal Bhavsar. Time Complexity in Deep Learning Models. Procedia Computer Science, 215:202--210, 2022. ISSN 1877-0509. doi: 10.1016/j.procs.2022.12.023. 4th International Conference on Innovative Data Communication Technology and Application. Feifei Shao, Long Chen, Jian Shao, Wei Ji, Shaoning Xiao, Lu Ye, Yueting Zhuang, and Jun Xiao. Deep Learning for Weakly-Supervised Object Detection and Localization: A Survey. Neurocomputing, 496:192--207, 2022. ISSN 0925-2312. doi: 10.1016/j.neucom.2022.01. 095. Wei Shen, Zelin Peng, Xuehui Wang, Huayu Wang, Jiazhong Cen, Dongsheng Jiang, Lingxi Xie, Xiaokang Yang, and Q. Tian. A Survey on Label-Efficient Deep Image Segmentation: Bridging the Gap Between Weak Supervision and Dense Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1--20, 2023. doi: 10.1109/TPAMI.2023.3246102. Connor Shorten and Taghi M. Khoshgoftaar. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(60): 1--48, 07 2019. ISSN 2196-1115. doi: 10.1186/s40537-019-0197-0. Javier Silvestre-Blanes, Teresa Albero-Albero, Ignacio Miralles, Rubén Pérez-Llorens, and Jorge Moreno. A Public Fabric Database for Defect Detection Methods and Results. Autex Research Journal, 19(4):363--374, 06 2019. doi: 10.2478/aut-2019-0035. C. Smith. Decision Trees and Random Forests: A Visual Introduction for Beginners. Blue Windmill Media, 2017. ISBN 9781549893759. URL https://books.google.com.co/books?id=Hi_CtAEACAAJ. Melvyn L. Smith, Lyndon N. Smith, and Mark F. Hansen. The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions. Computers in Industry, 130:103472, 2021. doi: 10.1016/j.compind. 2021.103472. Huazhu Song, Zichun Ding, Cuicui Guo, Zhe Li, and Hongxia Xia. Research on Combination Kernel Function of Support Vector Machine. In 2008 International Conference on Computer Science and Software Engineering, volume 1, pages 838--841, 2008. doi: 10.1109/CSSE.2008.1231. Mohsen Soori, Behrooz Arezoo, and Roza Dastres. Machine learning and artificial intelligence in CNC machine tools, A review. Sustainable Manufacturing and Service Economics, page 100009, 2023. ISSN 2667-3444. doi: 10.1016/j.smse.2023.100009. Miao Sun, Tony X. Han, Ming-Chang Liu, and Ahmad Khodayari-Rostamabad. Multiple instance learning convolutional neural networks for object recognition. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 3270--3275, 2016. doi: 10.1109/ICPR.2016.7900139. Zhiyuan Sun, Yunhao Yuan, Xiaoxiao Dong, Zhimei Liu, Kelong Cai, Wei Cheng, Jingjing Wu, Zhiyuan Qiao, and Aiguo Chen. Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder. International Journal of Clinical and Health Psychology, 23(4):100409, 2023. ISSN 1697-2600. doi: 10.1016/j.ijchp.2023.100409. Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, Inception-ResNet and the impact of residual connections on learning. AAAI Conference on Artificial Intelligence, 31, 02 2016. doi: 10.1609/aaai.v31i1.11231. Domen Tabernik, Samo Šela, Jure Skvarč, and Danijel Skočaj. Segmentation-Based Deep-Learning Approach for Surface-Defect Detection. Journal of Intelligent Manufacturing, May 2019. ISSN 1572-8145. doi: 10.1007/s10845-019-01476-x. Tomoumi Takase, Ryo Karakida, and Hideki Asoh. Self-paced Data Augmentation for Training Neural Networks, 2020. Michael W. Trosset, Carey E. Priebe, Youngser Park, and Michael I. Miller. Semisupervised learning from dissimilarity data. Computational Statistics & Data Analysis, 52(10):4643--4657, 2008. ISSN 0167-9473. doi: 10.1016/j.csda.2008.02.030. Marc van Kreveld, Tillmann Miltzow, Tim Ophelders, Willem Sonke, and Jordi L. Vermeulen. Between shapes, using the Hausdorff distance. Computational Geometry, 100, 2022. ISSN 0925-7721. doi: 10.1016/j.comgeo.2021.101817. Srikanth Vemula and Michael Frye. Mask R-CNN powerline detector: A deep learning approach with applications to a UAV. In 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), pages 1--6, 2020. doi: 10.1109/DASC50938.2020.9256456. R. Venkatesan and Li. Baoxin. Convolutional neural networks in visual computing : a concise guide. CRC press, first edition edition, 2017. ISBN 9781315154282 (ebook). URL https://lccn.loc.gov/2017029154. Eduardo Villegas-Jaramillo. Github-candies, 2023. URL https://github.com/ejvillegasj/candies. Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. Computational Analysis of Multiple Instance Learning-Based Systems for Automatic Visual Inspection: A Doctoral Research Proposal. In Sara Rodríguez, Javier Prieto, Pedro Faria, Sławomir Kłos, Alberto Fernández, Santiago Mazuelas, M. Dolores Jiménez-López, María N. Moreno, and Elena M. Navarro, editors, Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference, pages 374--377, Cham, 2019. Springer International Publishing. ISBN 978-3-319-99608-0. doi: 10.1007/978-3-319-99608-0_49. Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection, pages 67--99. IGI Global, 701 E. Chocolate Avenue Hershey PA, USA 17033, 2022. doi: 10.4018/978-1-6684-4991-2.ch004. Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. Candy classification using convolutional neural networks, data augmentation and transfer learning: Application and a new public dataset. In 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), pages 1--7, 2023a. doi: 10.1109/ICPRS58416.2023.10179072. Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. An Inexactly Supervised Methodology Based on Multiple Instance Learning, Convolutional Neural Networks and Dissimilarities for Interpretable Defect Detection and Localization on Textured Surfaces. IEEE Access, 11:138229--138246, 2023b. doi: 10.1109/ACCESS.2023.3340047. Eduardo Villegas-Jaramillo, Ana Lorena Uribe-Hurtado, and Mauricio Orozco-Alzate. Multi-core Parallelization of Point Set Dissimila- rities for Accelerating the Comparison of Bags with Many Instances. In Sigeru Omatu, Rashid Mehmood, Pawel Sitek, Serafino Cicerone, and Sara Rodríguez, editors, Distributed Computing and Artificial Intelligence, 19th International Conference, pages 208--218, Cham, 2023. Springer International Publishing. ISBN 978-3-031-20859-1. A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis. Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 2018:13, 2018. ISSN 1687-5265. doi: 10.1155/2018/7068349. Hua Wang, Cuiqin Ma, and Lijuan Zhou. A Brief Review of Machine Learning and Its Application. In 2009 International Conference on Information Engineering and Computer Science, pages 1--4, 2009. doi: 10.1109/ICIECS.2009.5362936. Juan Wang and Bin Xia. Weakly supervised image segmentation beyond tight bounding box annotations. Computers in Biology and Medicine, 169:107913, 2024. ISSN 0010-4825. doi: 10.1016/j.compbiomed.2023.107913. URL https://www.sciencedirect.com/ science/article/pii/S0010482523013781. Shenlong Wang, Kaixin Han, and Jiafeng Jin. Review of image low-level feature extraction methods for content-based image retrieval. Sensor Review, ahead-of-print, 08 2019a. doi: 10.1108/SR-04-2019-0092. Wei Wang, Linyang He, Guohua Cheng, Ting Wen, and Yan Tian. Learning from ambiguous labels for X-Ray security inspection via weakly supervised correction. Multimedia Tools and Applications, 83:1--16, 05 2023. doi: 10.1007/s11042-023-15299-9. Xinggang Wang, Yongluan Yan, Peng Tang, Wenyu Liu, and Xiaojie Guo. Bag similarity network for deep multi-instance learning. Information Sciences, 504:578--588, 2019b. ISSN 0020-0255. doi: 10.1016/j.ins.2019.07.071. Xin Wen, Jvran Shan, Yu He, and Kechen Song. Steel Surface Defect Recognition: A Survey. Coatings, 13(1), 2023. ISSN 2079-6412. doi: 10.3390/coatings13010017. Xinquan Wu and Xuefeng Yan. A spatial pyramid pooling-based deep reinforcement learning model for dynamic job-shop scheduling problem. Computers & Operations Research, 160:106401, 2023. ISSN 0305-0548. doi: 10.1016/j.cor.2023.106401. Ting Xiao, Lei Liu, Kai Li, Wenjian Qin, Nicolas Yu, and Zhi-Cheng Li. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. BioMed Research International, 2018:1--9, 06 2018. doi: 10.1155/2018/4605191. Xin Xu and Eibe Frank. Logistic regression and boosting for labeled bags of instances. In Honghua Dai, Ramakrishnan Srikant, and Chengqi Zhang, editors, Advances in Knowledge Discovery and Data Mining, pages 272--281, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg. ISBN 978-3-540-24775-3. doi: 10.1007/978-3-540-24775-3_35. Yan Xu, Yeshu Li, Zhengyang Shen, Ziwei Wu, Teng Gao, Yubo Fan, Maode Lai, and Eric Chang. Parallel multiple instance learning for extremely large histopathology image analysis. BMC Bioinformatics, 18, 08 2017. doi: 10.1186/s12859-017-1768-8. Mingqiang Yang, Kidiyo Kpalma, and Joseph Ronsin. A Survey of Shape Feature Extraction Techniques. IN-TECH, 15, 11 2007. doi: 10.5772/6237. Chia-Yu Yen and Krzysztof J. Cios. Image recognition system based on novel measures of image similarity and cluster validity. Neurocomputing, 72(1):401--412, 2008. ISSN 0925-2312. doi: 10.1016/j.neucom.2007.12.018. Machine Learning for Signal Processing (MLSP 2006) / Life System Modelling, Simulation, and Bio-inspired Computing (LSMS 2007). Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, and Antonio Plaza. Optical Remote Sensing Image Understanding With Weak Supervision: Concepts, methods, and perspectives. IEEE Geoscience and Remote Sensing Magazine, 10 (2):250--269, 2022. doi: 10.1109/MGRS.2022.3161377. Amelia Zafra and Eva Gibaja. Nearest neighbor-based approaches for multi-instance multi-label classification. Expert Systems with Applications, 232:120876, 2023. ISSN 0957-4174. doi: 10.1016/j.eswa.2023.120876. Dingwen Zhang, Junwei Han, Gong Cheng, and Ming-Hsuan Yang. Weakly Supervised Object Localization and Detection: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5866--5885, 2022. doi: 10.1109/TPAMI.2021.3074313. W. J. Zhang, D. Li, F. Ye, and H. Sun. Automatic optical defect inspection and dimension measurement of drill bit. In 2006 International Conference on Mechatronics and Automation, pages 95--100, 2006. doi: 10.1109/ICMA.2006.257459. Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning Deep Features for Discriminative Localization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2921--2929, 2016. doi: 10.1109/CVPR.2016.319. Lei Zhou, Yu Zhao, Jie Yang, Qi Yu, and Xun Xu. Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Processing, 12(4):563--571, 2018. ISSN 1751-9659. doi: 10.1049/iet-ipr.2017.0636. Tongxue Zhou, Su Ruan, and Stéphane Canu. A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 3-4:100004, sep 2019. ISSN 2590-0056. doi: 10.1016/j.array.2019.100004. Zhi-Hua Zhou. A Brief Introduction to Weakly Supervised Learning. National Science Review, 1(August 2017):44--53, 2017. ISSN 2095-5138. doi: 10.1093/nsr/nwx106. Keqian Zhu and Jingfei Jiang. Research on Parallel Acceleration for Deep Learning Inference Based on Many-Core ARM Platform. In Chao Li and Junjie Wu, editors, Advanced Computer Architecture, pages 30--41, Singapore, 2018. Springer Singapore. ISBN 978-981-13-2423-9. doi: 10.1007/978-981-13-2423-9_3. Justus Zipfel, Felix Verworner, Marco Fischer, Uwe Wieland, Mathias Kraus, and Patrick Zschech. Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models. Computers & Industrial Engineering, 177: 109045, 2023. ISSN 0360-8352. doi: 10.1016/j.cie.2023.109045. Łukasz Struski, Szymon Janusz, Jacek Tabor, Michał Markiewicz, and Arkadiusz Lewicki. Multiple instance learning for medical image classification based on instance importance. Biomedical Signal Processing and Control, 91:105874, 2024. ISSN 1746-8094. doi: 10.1016/j.bspc.2023.105874. URL https://www.sciencedirect.com/science/article/pii/S1746809423013071
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería y Arquitectura
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Orozco-Alzate, Mauricio854bb7da044fd757cf5087bb23930d6d600Villegas Jaramillo, Eduardo José51875293c3f899475ead0a7bb37163c2Gaia Grupo de Ambientes Inteligentes AdaptativosVillegas Jaramillo, Eduardo José [https://orcid.org/0000-0002-7563-2913]Villegas Jaramillo, Eduardo José [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000625698]2024-05-17T21:31:13Z2024-05-17T21:31:13Z2024https://repositorio.unal.edu.co/handle/unal/86112Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasEl propósito de la inspección visual automática es detectar y localizar defectos en diferentes tipos de objetos y superficies. Tradicionalmente, estos procesos eran llevados a cabo de manera manual por expertos humanos. Sin embargo, las técnicas de inspección manual suelen ser lentas e ineficientes, encontrando que en muchos casos, no cumplen adecuadamente con las demandas de producción en diversas áreas. A lo largo del tiempo, se han empleado diferentes soluciones para abordar este problema, centradas principalmente en el procesamiento de imágenes y en técnicas clásicas para la extracción de características, el reconocimiento de patrones, y el uso de clasificadores como máquinas de vectores de soporte, la regla del vecino más cercano y árboles de decisión, entre otros. Recientemente, se ha logrado resolver este problema mediante técnicas de aprendizaje profundo, arrojando resultados muy prometedores. No obstante, se han identificado ciertas limitaciones, tales como la necesidad de contar con un conjunto de datos extenso para el entrenamiento, los elevados requisitos computacionales y la falta de claridad en la interpretación de los resultados. En esta tesis se explora el empleo de diversas técnicas que incorporan el aprendizaje profundo para abordar problemas de inspección visual automática en la producción de distintos objetos, tales como láminas de vidrio, dulces, telas y conjuntos sintéticos de superficies con textura. Además, ante las limitaciones observadas en las técnicas que hacen uso de aprendizaje profundo, con un enfoque especial en la interpretabilidad, se propone una metodología basada en técnicas de aprendizaje inexactamente supervisado. Esta metodología tiene como objetivo realizar la detección y localización de defectos en diversos problemas de inspección visual automática. La metodología se enfoca en superar y solucionar algunos de los desafíos que surgen al entrenar diferentes modelos cuando no se dispone de información precisa de las etiquetas. Para ello, se integran técnicas provenientes del aprendizaje inexactamente supervisado, como el aprendizaje de múltiples instancias (MIL) y el aprendizaje profundo (DL). Adicionalmente, la utilización de disimilitudes y clasificadores simples, como el del vecino más cercano ($k$-NN), contribuye al desarrollo y entrenamiento de sistemas capaces de distinguir entre productos defectuosos y no defectuosos, proporcionando la interpretación gráfica correspondiente. La metodología desarrollada fue evaluada en diversos escenarios con diferentes conjuntos de datos, abarcando tanto conjuntos sintéticos como conjuntos de imágenes reales, mayoritariamente compuestos por superficies texturizadas. Los resultados obtenidos fueron positivos, destacándose varias fortalezas clave de la metodología tales como la capacidad de trabajar con imágenes débilmente etiquetadas, la adaptabilidad para conjuntos de datos con pocas imágenes o desbalanceados, la detección gráfica multiresolución de defectos, la implementación de una ventana deslizante para la generación de bolsas y, finalmente, la habilidad de interpretar de manera gráfica los resultados obtenidos. En cuanto al análisis computacional, es relevante resaltar que las redes neuronales convolucionales (CNN) representan la carga computacional más significativa, ya sea en el entrenamiento del modelo, en la extracción de características o en la predicción de la etiqueta de un objeto de prueba. No obstante, los análisis de desempeño temporal indican que la metodología puede ser aplicada de manera efectiva en diversos contextos (Texto tomado de la fuente)The purpose of automatic visual inspection is to detect and locate defects in different types of objects and surfaces. Traditionally, these processes were carried out manually by human experts. However, manual inspection techniques are usually slow and inefficient, finding that in many cases, they do not adequately meet production demands in various areas. Over time, different solutions have been used to address this problem, mainly focused on image processing and classical techniques for feature extraction, pattern recognition, and the use of classifiers such as support vector machines, the nearest neighbor rule and decision trees, among others. Recently, this problem has been solved using deep learning techniques, yielding very promising results. However, certain limitations have been identified, such as the need of an extensive dataset for training, high computational requirements, and lack of clarity in the interpretation of results. This thesis explores the use of various techniques that incorporate deep learning to address automatic visual inspection problems in the production of different objects, such as glass sheets, candies, fabrics, and synthetic sets of textured surfaces. Furthermore, given the limitations observed in techniques that use deep learning, with a special focus on interpretability, a methodology based on inexactly supervised learning techniques is proposed. This methodology aims to detect and localize defects in various automatic visual inspection problems. The methodology focuses on overcoming and solving some of the challenges that arise when training different models when accurate label information is not available. To do this, techniques from weakly supervised learning are integrated, such as multiple instance learning (MIL) and deep learning (DL). Additionally, the use of dissimilarities and simple classifiers, such as the nearest neighbor ($k$-NN), contributes to the development and training of systems capable of distinguishing between defective and non-defective products, providing the corresponding graphical interpretation. The developed methodology was evaluated in various scenarios with different datasets, covering both synthetic sets and real image sets, mostly composed of textured surfaces. The results obtained were positive, highlighting several key strengths of the methodology such as the ability to work with weakly labeled images, adaptability for datasets with few or unbalanced images, multi-resolution graphical detection of defects, the implementation of a sliding window for generating bags and, finally, the ability to graphically interpret the results obtained. Regarding computational analysis, it is relevant to highlight that convolutional neural networks (CNN) represent the most significant computational load, whether in model training, feature extraction or in predicting the label of a test object. However, temporal performance analyses indicate that the methodology can be effectively applied in various contexts.DoctoradoDoctor en IngenieríaIndustrial, Organizaciones Y Logística.Sede Manizalesxvii, 147 páginasapplication/pdfspaUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Industria y OrganizacionesFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales670 - Manufactura::679 -Otros productos de materiales específicosInspección visual automáticaAprendizaje de múltiples instanciasDescomposición en bloquesExtracción de característicasInterpretación gráficaSupervisión inexactaDisimilitudesLocalización de defectosAutomatic visual inspectionMultiple instance learningBlock decompositionFeature extractionGraphical interpretationInexact supervisionDissimilaritiesDefect localizationMetodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisadoHeterogeneous methodology for automatic visual inspection based on inexactly supervised learning techniquesTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06TextAliyu Abubakar, Mohammed Ajuji, and Ibrahim Usman Yahya. Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination. Applied System Innovation, 3(2), 2020. ISSN 2571-5577. doi: 10.3390/asi3020020.Ethem Alpaydin, Veronika Cheplygina, Marco Loog, and David M.J. Tax. Single- vs. multiple-instance classification. Pattern Recognition, 48(9):2831--2838, 2015. ISSN 00313203. doi: 10.1016/j.patcog.2015.04.006.Jaume Amores. Multiple instance classification: Review, taxonomy and comparative study. Artificial Intelligence, 201:81--105, 2013. ISSN 0004-3702. doi: 10.1016/j.artint.2013.06.003.Katharina Anding, Lilli Haar, Galina Polte, Jurij Walz, and Gunther Notni. Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks. In Proceedings Volume 11144, Photonics and Education in Measurement Science 2019, page 26, 09 2019. doi: 10.1117/12.2530899.Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. Support Vector Machines for Multiple-Instance Learning. Advances in Neural Information Processing Systems, 15:561--568, 01 2002.Massimo Aria and Corrado Cuccurullo. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4):959--975, 2017. doi: 10.1016/j.joi.2017.08.007.Boris Babenko. Multiple Instance Learning: Algorithms and Applications. In Computer Science, Mathematics, 01 2008. URL https://api.semanticscholar.org/CorpusID:2153770.Saber Mirzaee Bafti, Chee Siang Ang, Md Moinul Hossain, Gianluca Marcelli, Marc Alemany-Fornes, and Anastasios D. Tsaousis. A crowdsourcing semi-automatic image segmentation platform for cell biology. Computers in Biology and Medicine, 130:104204, 2021. ISSN 0010-4825. doi: 10.1016/j.compbiomed.2020.104204.Ganbayar Batchuluun, Jiho Choi, and Kang Ryoung Park. CAM-CAN: Class activation map-based categorical adversarial network. Expert Systems with Applications, 222:119809, 2023. ISSN 0957-4174. doi: 10.1016/j.eswa.2023.119809.Laura Lucía Becerra Elejalde. Supercoco se renueva tras 70 años e incursiona con agua de coco en su portafolio. La República, June 16th, 2020. https://tinyurl.com/43dsd9ad.Samia Benyahia, Boudjelal Meftah, and Olivier Lézoray. Multi-features extraction based on deep learning for skin lesion classification. Tissue and Cell, 74:101701, 2022. ISSN 0040-8166. doi: 10.1016/j.tice.2021.101701.Jeff Bier. Is deep learning the solution to all computer vision problems? Blog post at the Vision Systems Design website, 2019. URL https://tinyurl.com/bdf8yfw2Tiago Botari, Rafael Izbicki, and Andre C. P. L. F. de Carvalho. Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space. In Peggy Cellier and Kurt Driessens, editors, Machine Learning and Knowledge Discovery in Databases, pages 241--252, Cham, 2020. Springer International Publishing. ISBN 978-3-030-43823-4.Jakob Božič, Domen Tabernik, and Danijel Skočaj. End-to-end training of a two-stage neural network for defect detection. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 5619--5626, 2021. doi: 10.1109/icpr48806.2021.9412092.Jakob Božič, Domen Tabernik, and Danijel Skočaj. Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Computers in Industry, 129:103459, 08 2021. doi: 10.1016/j.compind.2021.103459.Max Bramer. Principles of Data Mining. Springer, fourth edition edition, 2020. ISBN 978-1-4471-7493-6. doi: 10.1007/978-1-4471-7493-6. Gilles Brassard and Paul Bratley. Fundamentos de Algoritmia. Prentice Hall, Montreal, primera edition, 1997. ISBN 84-89660-00-X.Marc André Carbonneau, Veronika Cheplygina, Eric Granger, and Ghyslain Gagnon. Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognition, 77:329--353, 2017. ISSN 00313203. doi: 10.1016/j.patcog.2017.10.009.Ssu-Han Chen and Der-Baau Perng. Directional textures auto-inspection using principal component analysis. International Journal of Advanced Manufacturing Technology, 55:1099--1110, 08 2011. doi: 10.1007/s00170-010-3141-1.Wen-Chin Chen and Shou-Wen Hsu. A neural-network approach for an automatic LED inspection system. Expert Systems with Applications, 33:531--537, 08 2007. doi: 10.1016/j.eswa.2006.06.011.Min-Yuan Cheng, Riqi Radian Khasani, and Kent Setiono. Image quality enhancement using HybridGAN for automated railway track defect recognition. Automation in Construction, 146:104669, 2023. ISSN 0926-5805. doi: 10.1016/j.autcon.2022.104669.Veronika Cheplygina and David M. J. Tax. Characterizing Multiple Instance Datasets. In Similarity-Based Pattern Recognition, pages 15--27, Cham, 2015. Springer International Publishing. ISBN 978-3-319-24261-3. doi: 10.1007/978-3-319-24261-3_2.Veronika Cheplygina, David M.J. Tax, and Marco Loog. Multiple instance learning with bag dissimilarities. Pattern Recognition, 48 (1):264--275, 2015. ISSN 0031-3203. doi: 10.1016/j.patcog.2014.07.022.Veronika Cheplygina, David M. J. Tax, and Marco Loog. Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Transactions on Neural Networks and Learning Systems, 27(6):1379--1391, June 2016. ISSN 2162-237X. doi: 10.1109/TNNLS.2015. 2424254.François Chollet. Deep Learning with Python. Manning Publications, 2 edition, October 2021. ISBN 9781617296864. URL https://www.manning.com/books/deep-learning-with-python-second-edition.Roger Chugh, Vardaan Bhatia, Karan Khanna, and Vandana Bhatia. A Comparative Analysis of Classifiers for Image Classification. In 2020 - 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 248--253, 01 2020. doi: 10.1109/Confluence47617.2020.9058042.Yandre Costa, Diego Bertolini, Alceu Jr, George Cavalcanti, and Luiz Soares de Oliveira. The dissimilarity approach: a review. Artificial Intelligence Review, 53:2783--2808, 04 2020. doi: 10.1007/s10462-019-09746-z.Esteban Cumbajin, Nuno Rodrigues, Paulo Costa, Rolando Miragaia, Luís Frazão, Nuno Costa, Antonio Fernández-Caballero, Jorge Carneiro, Leire H. Buruberri, and António Pereira. A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection. Journal of Imaging, 9(10), 2023. ISSN 2313-433X. doi: 10.3390/jimaging9100193.Suresh Dara and Priyanka Tumma. Feature Extraction By Using Deep Learning: A Survey. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 1795--1801, 2018. doi: 10.1109/ICECA.2018.8474912.Shubhabrata Datta and J. Paulo Davim. Machine Learning in Industry. Management and Industrial Engineering. Springer, Cham, Switzerland, 2022. ISBN 978-3-030-75846-2. doi: 10.1007/978-3-030-75847-9.Soham De, Anirbit Mukherjee, and Enayat Ullah. Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration. arXiv, 2018. doi: 10.48550/ARXIV.1807.06766.Deltamax Automazione . Electronic References, 2016. URL http://www.deltamaxautomazione.it/risolvi/.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248--255, 2009. doi: 10.1109/CVPR.2009.5206848.Thomas G. Dietterich, Richard H. Lathrop, and Tomás Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89:31--71, 1997. ISSN 0004-3702/97.Peilei Dong and Wenmin Wang. Better region proposals for pedestrian detection with R-CNN. In 2016 Visual Communications and Image Processing (VCIP), pages 1--4, 2016. doi: 10.1109/VCIP.2016.7805452.Robert P W Duin. Dissimilarity measures , Pattern Recognition Tools, 2012. URL http://37steps.com/1264/dissimilarity-measures/.Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, and Marco Loog. Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance. In Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, and Marcello Pelillo, editors, Structural, Syntactic, and Statistical Pattern Recognition, pages 183--192, Berlin, Heidelberg, 2014. Springer Berlin Heidelberg. ISBN 978-3-662-44415-3. doi: 10.1007/978-3-662-44415-3_19.Robert P.W. Duin and Elzbieta Pekalska. The dissimilarity representation for pattern recognition, a tutorial. http://rduin.nl/ presentations/DisRep_Tutorial.pdf, 2011.Ross Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440--1448, 2015. doi: 10.1109/ICCV.2015.169.Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 580--587, 2014. doi: 10.1109/CVPR.2014.81.Facundo E. Godoy. Métodos clásicos de clasificación: comparación y aplicación. Trabajo especial licenciatura en matemática, Facultad de Matemática, Astronomía, Física y Computación. Universidad Nacional de Córdoba, July 2021.Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.Luiz G. Hafemann, Luiz S. Oliveira, Paulo R. Cavalin, and Robert Sabourin. Transfer learning between texture classification tasks using Convolutional Neural Networks. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1--7, 2015. doi: 10.1109/IJCNN.2015.7280558.Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. In David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, editors, Computer Vision -- ECCV 2014, pages 346--361, Cham, 2014. Springer International Publishing. ISBN 978-3-319-10578-9.Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2980--2988, 2017. doi: 10.1109/ICCV.2017.322.Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, and Sarah Vluymans. Multiple instance learning: Foundations and algorithms. Springer International Publishing, 2016. ISBN 9783319477596. doi: 10.1007/978-3-319-47759-6.Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, Malcolm Slaney, Ron J. Weiss, and Kevin Wilson. CNN Architectures for Large-Scale Audio Classification, 2017.O. Hmidani and E. M. Ismaili Alaoui. A comprehensive survey of the R-CNN family for object detection. In 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), pages 1--6, 2022. doi: 10.1109/CommNet56067. 2022.9993862.Rudolf Hoffmann and Christoph Reich. A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing. Electronics, 12(22), 2023. ISSN 2079-9292. doi: 10.3390/electronics12224572.Shih-Chung Hsu, Chung-Lin Huang, and Cheng-Hung Chuang. Vehicle detection using simplified fast R-CNN. In 2018 International Workshop on Advanced Image Technology (IWAIT), pages 1--3, 2018. doi: 10.1109/IWAIT.2018.8369767.Shiluo Huang, Zheng Liu, Wei Jin, and Ying Mu. Bag dissimilarity regularized multi-instance learning. Pattern Recognition, 126: 108583, 2022. ISSN 0031-3203. doi: 10.1016/j.patcog.2022.108583.Yibin Huang, Congying Qiu, Xiaonan Wang, Shijun Wang, and Kui Yuan. A Compact Convolutional Neural Network for Surface Defect Inspection. Sensors, 20(7), 2020. ISSN 1424-8220. doi: 10.3390/s20071974. URL https://www.mdpi.com/1424-8220/20/7/1974.Dejice Jacob, Phil Trinder, and Jeremy Singer. Python programmers have GPUs too: automatic Python loop parallelization with staged dependence analysis. In Stefan Marr and Juan Fumero, editors, Proceedings of the 15th ACM SIGPLAN International Symposium on Dynamic Languages, DLS 2019, Athens, Greece, October 20, 2019, pages 42--54. ACM, 2019. ISBN 978-1-4503-6996-1. doi: 10.1145/3359619.3359743.Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip Yu, and Yue Zhao. Weakly Supervised Anomaly Detection: A Survey, 02 2023.Manjunath Jogin, Mohana, M S Madhulika, G D Divya, R K Meghana, and S Apoorva. Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), pages 2319--2323, 2018. doi: 10.1109/RTEICT42901.2018.9012507.M. Keshavarzi, Mohammad Ali Dehghan, and Mashaallah Mashinchi. Classification based on similarity and dissimilarity through equivalence classes. Applied and Computational Mathematics, 8:203--215, 01 2009.Salman Khan, Hossein Rahmani, and Syed Afaq Ali Shah. A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers, 2018. ISBN 1681730219.S. Kornblith, J. Shlens, and Q. V. Le. Do Better ImageNet Models Transfer Better? In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2656--2666, Los Alamitos, CA, USA, jun 2019. IEEE Computer Society. doi: 10.1109/CVPR.2019.00277.Bangjun Lei, Guangzhu Xu, Ming Feng, Yaobin Zou, Ferdinand Van der Heijden, Dick de Ridder, and David M. J. Tax. Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. Wiley, 2017. doi: 10.1002/9781119152484.Christian Leistner, Amir Saffari, and Horst Bischof. MIForests: Multiple-Instance Learning with Randomized Trees. In Kostas Daniilidis, Petros Maragos, and Nikos Paragios, editors, Computer Vision -- ECCV 2010, pages 29--42, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg. ISBN 978-3-642-15567-3. doi: 10.1007/978-3-642-15567-3_3.Li Li, Zuopai Zhou, Na Bai, Tao Wang, Kan-Hao Xue, Huajun Sun, Qiang He, Weiming Cheng, and Xiangshui Miao. Naive Bayes classifier based on memristor nonlinear conductance. Microelectronics Journal, 129:105574, 2022. ISSN 0026-2692. doi: 10.1016/j.mejo.2022.105574.Pengcheng Li, Zihao Dong, Jianjie Shi, Zengzhi Pang, and Jinping Li. Detection of Small Size Defects in Belt Layer of Radial Tire Based on Improved Faster R-CNN. In 2021 11th International Conference on Information Science and Technology (ICIST), pages 531--538, 2021a. doi: 10.1109/ICIST52614.2021.9440580.Shengyuan Li and Xuefeng Zhao. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Advances in Civil Engineering, 2019:1--12, 04 2019. doi: 10.1155/2019/6520620.Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, and Dejing Dou. Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond, 2021b.Yu Li and ZiWei Wang. Research on Textile Defect Detection Based on Improved Cascade R-CNN. In 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), pages 43--46, 2021. doi: 10.1109/AIEA53260.2021.00017.Yu Liang, Siguang Li, Chungang Yan, Maozhen Li, and Changjun Jiang. Explaining the black-box model: A survey of local interpretation methods for deep neural networks. Neurocomputing, 419:168--182, 2021. ISSN 0925-2312. doi: 10.1016/j.neucom.2020.08.011.Hong-Dar Lin and Duan-Cheng Ho. Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems. The International Journal of Advanced Manufacturing Technology, 34:567--583, 09 2007. doi: 10.1007/s00170-006-0614-3.Bo Liu, Zhi-Feng Hao, and Xiao-Wei Yang. Nesting support vector machinte for muti-classification [machinte read machine]. In 2005 International Conference on Machine Learning and Cybernetics, volume 7, pages 4220--4225 Vol. 7, 2005. doi: 10.1109/ICMLC. 2005.1527678.Caihui Liu, Bowen Lin, Jianying Lai, and Duoqian Miao. An improved decision tree algorithm based on variable precision neighborhood similarity. Information Sciences, 615:152--166, 2022a. ISSN 0020-0255. doi: 10.1016/j.ins.2022.10.043.Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. SSD: Single Shot MultiBox Detector. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision — ECCV 2016, pages 21--37, Cham, 2016. Springer International Publishing. doi: 10.1007/978-3-319-46448-0_2.Wei Liu, Jiarui Zhang, and Yijun Zhao. A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans. In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pages 998--1001, 2022b. doi: 10.1109/COMPSAC54236.2022.00154.Tanya Makkar, Yogesh Kumar, Ashwani Kr Dubey, Álvaro Rocha, and Ayush Goyal. Analogizing time complexity of KNN and CNN in recognizing handwritten digits. In 2017 Fourth International Conference on Image Information Processing (ICIIP), pages 1--6, 2017. doi: 10.1109/ICIIP.2017.8313707.Elias N. Malamas, Euripides G M Petrakis, Michalis Zervakis, Laurent Petit, and Jean Didier Legat. A survey on industrial vision systems, applications and tools. Image and Vision Computing, 21(2):171--188, 2003. ISSN 0262-8856/03.Borja Marin, Keith Brown, and Mustafa S. Erden. Automated Masonry crack detection with Faster R-CNN. In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pages 333--340, 2021. doi: 10.1109/CASE49439.2021. 9551683.Oded Maron and Aparna Lakshmi Ratan. Multiple-Instance Learning for Natural Scene Classification. In Proceedings of the Fifteenth International Conference on Machine Learning, ICML ’98, page 341–349, San Francisco, CA, USA, 1998. Morgan Kaufmann Publishers Inc. ISBN 1558605568. URL https://dl.acm.org/doi/10.5555/645527.657299.Carlos Mera. Detección de defectos en sistemas de inspección visual automática a través del aprendizaje de múltiples instancias. Tesis doctoral, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia, may 2017. URL https://repositorio.unal. edu.co/handle/unal/59346. Doctorate in Engineering - Systems and Informatics.Carlos Mera, Mauricio Orozco-Alzate, John Branch, and Domingo Mery. Automatic visual inspection: An approach with multi-instance learning. Computers in Industry, 83:46--54, 2016. doi: 10.1016/j.compind.2016.09.002.Carlos Mera, Mauricio Orozco-Alzate, and John Branch. Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection. Computers in Industry, 109:153--164, aug 2019. doi: 10.1016/j.compind.2019.04.006.Domingo Mery. BALU: A Matlab toolbox for computer vision, pattern recognition and image processing, 2011. URL http://dmery.ing. puc.cl/index.php/balu/.Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267:1--38, 2019. ISSN 0004-3702. doi: 10.1016/j.artint.2018.07.007.H. B. Mitchell. Image Similarity Measures, pages 167--185. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. ISBN 978-3-642-11216-4. doi: 10.1007/978-3-642-11216-4_14.Elhassan Mohamed, Konstantinos Sirlantzis, and Gareth Howells. A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation. Displays, 73:102239, 2022. ISSN 0141-9382. doi: 10.1016/j.displa.2022.102239.Divya Pramasani Mohandoss, Yong Shi, and Kun Suo. Outlier Prediction Using Random Forest Classifier. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pages 0027--0033, 2021. doi: 10.1109/CCWC51732.2021. 9376077.Christoph Molnar. Interpretable Machine Learning. Independently published, 2019. URL https://christophm.github.io/ interpretable-ml-book/.Romain Mormont, Pierre Geurts, and Raphaël Marée. Comparison of Deep Transfer Learning Strategies for Digital Pathology. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 2343--2352, 2018. doi: 10.1109/CVPRW.2018.00303.Saad Naeem, Noreen Jamil, Habib Ullah Khan, and Shah Nazir. Complexity of Deep Convolutional Neural Networks in Mobile Computing. Complexity, 2020:3853780, Sep 2020. ISSN 1076-2787. doi: 10.1155/2020/3853780.Tam Nguyen and Raviv Raich. Incomplete Label Multiple Instance Multiple Label Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3):1320--1337, 2022. doi: 10.1109/tpami.2020.3017456.Mark Nixon and Alberto Aguado. Low-level feature extraction (including edge detection), pages 137--216. Photonics and Education in Measurement Science, 12 2012. ISBN 9780123965493. doi: 10.1016/B978-0-12-396549-3.00004-5.C. Guadalupe Origel-Rivas, Eréndira Rendón Lara, Itzel Abundez Barrera, and Roberto Alejo Eleuterio. Redes neuronales artificiales y árboles de decisión para la clasificación con datos categóricos. Res. Comput. Sci., 149(8):541--554, 2020.Taiwo R. Oyedare, Vijay Shah, Daniel Jakubisin, and Jeffrey Reed. Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks. arXiv - EE - Signal Processing, 03 2023. doi: 10.48550/arXiv.2303.03326.Christian Payer, Darko Štern, Horst Bischof, and Martin Urschler. Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Medical Image Analysis, 54:207--219, 2019. ISSN 1361-8415. doi: 10.1016/j.media.2019.03.007.Elzbieta Pekalska and Robert P. W. Duin. The dissimilarity representation for pattern recognition , Foundations and Applications. World Scientific, Delft, 2005. ISBN 9812565302. doi: 10.1142/9789812703170.Jacek Piotrowski. Top-Down Approach to Image Similarity Measures. In Leonard Bolc, Juliusz L. Kulikowski, and Konrad Woj- ciechowski, editors, Computer Vision and Graphics, pages 66--69, Berlin, Heidelberg, 2009. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-02345-3_7.Leonardo Plotegher, Chiara Corridori, Fabio Dolci, Claudio Andreatta, Silvio Benini, and Matteo Devilli. The GI dataset for glass inspection 1st release (August 2016), 2016. URL http://www.deltamaxautomazione.it/risolvi.Diana Porro-Munoz, Robert P W Duin, Isneri Talavera, and Mauricio Orozco-Alzate. Classification of three-way data by the dissimilarity representation. Signal Processing, 91(11):2520--2529, 2011. ISSN 01651684. doi: 10.1016/j.sigpro.2011.05.004.Konpat Preechakul, Sira Sriswasdi, Boonserm Kijsirikul, and Ekapol Chuangsuwanich. Improved image classification explainability with high-accuracy heatmaps. iScience, 25(3):103933, 2022. ISSN 2589-0042. doi: 10.1016/j.isci.2022.103933.Q.R. Razlighi, N. Kehtarnavaz, and S. Yousefi. Evaluating similarity measures for brain image registration. Journal of Visual Communication and Image Representation, 24(7):977--987, 2013. ISSN 1047-3203. doi: 10.1016/j.jvcir.2013.06.010.Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779--788, 2016. doi: 10.1109/CVPR.2016.91.Payam Refaeilzadeh, Lei Tang, and Huan Liu. Cross-Validation, pages 532--538. Springer US, Boston, MA, 2009. ISBN 978-0-387- 39940-9. doi: 10.1007/978-0-387-39940-9_565.Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(06):1137--1149, jun 2017. ISSN 1939-3539. doi: 10.1109/TPAMI.2016.2577031.Amal Saadallah, Jan Buescher, Omar Abdulaaty, Thorben Panusch, Jochen Deuse, and Katharina Morik. Explainable Predictive Quality Inspection using Deep Learning in Electronics Manufacturing. Procedia CIRP, 107:594--599, 01 2022. doi: 10.1016/j.procir.2022.05.031.Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision, 128(2): 336--359, Feb 2020. ISSN 1573-1405. doi: 10.1007/s11263-019-01228-7.Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, and Yann LeCun. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. International Conference on Learning Representations (ICLR) Banff, 2013. doi: 10.48550/ARXIV.1312.6229.Bhoomi Shah and Hetal Bhavsar. Time Complexity in Deep Learning Models. Procedia Computer Science, 215:202--210, 2022. ISSN 1877-0509. doi: 10.1016/j.procs.2022.12.023. 4th International Conference on Innovative Data Communication Technology and Application.Feifei Shao, Long Chen, Jian Shao, Wei Ji, Shaoning Xiao, Lu Ye, Yueting Zhuang, and Jun Xiao. Deep Learning for Weakly-Supervised Object Detection and Localization: A Survey. Neurocomputing, 496:192--207, 2022. ISSN 0925-2312. doi: 10.1016/j.neucom.2022.01. 095.Wei Shen, Zelin Peng, Xuehui Wang, Huayu Wang, Jiazhong Cen, Dongsheng Jiang, Lingxi Xie, Xiaokang Yang, and Q. Tian. A Survey on Label-Efficient Deep Image Segmentation: Bridging the Gap Between Weak Supervision and Dense Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1--20, 2023. doi: 10.1109/TPAMI.2023.3246102.Connor Shorten and Taghi M. Khoshgoftaar. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(60): 1--48, 07 2019. ISSN 2196-1115. doi: 10.1186/s40537-019-0197-0.Javier Silvestre-Blanes, Teresa Albero-Albero, Ignacio Miralles, Rubén Pérez-Llorens, and Jorge Moreno. A Public Fabric Database for Defect Detection Methods and Results. Autex Research Journal, 19(4):363--374, 06 2019. doi: 10.2478/aut-2019-0035.C. Smith. Decision Trees and Random Forests: A Visual Introduction for Beginners. Blue Windmill Media, 2017. ISBN 9781549893759. URL https://books.google.com.co/books?id=Hi_CtAEACAAJ.Melvyn L. Smith, Lyndon N. Smith, and Mark F. Hansen. The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions. Computers in Industry, 130:103472, 2021. doi: 10.1016/j.compind. 2021.103472.Huazhu Song, Zichun Ding, Cuicui Guo, Zhe Li, and Hongxia Xia. Research on Combination Kernel Function of Support Vector Machine. In 2008 International Conference on Computer Science and Software Engineering, volume 1, pages 838--841, 2008. doi: 10.1109/CSSE.2008.1231.Mohsen Soori, Behrooz Arezoo, and Roza Dastres. Machine learning and artificial intelligence in CNC machine tools, A review. Sustainable Manufacturing and Service Economics, page 100009, 2023. ISSN 2667-3444. doi: 10.1016/j.smse.2023.100009.Miao Sun, Tony X. Han, Ming-Chang Liu, and Ahmad Khodayari-Rostamabad. Multiple instance learning convolutional neural networks for object recognition. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 3270--3275, 2016. doi: 10.1109/ICPR.2016.7900139.Zhiyuan Sun, Yunhao Yuan, Xiaoxiao Dong, Zhimei Liu, Kelong Cai, Wei Cheng, Jingjing Wu, Zhiyuan Qiao, and Aiguo Chen. Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder. International Journal of Clinical and Health Psychology, 23(4):100409, 2023. ISSN 1697-2600. doi: 10.1016/j.ijchp.2023.100409.Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, Inception-ResNet and the impact of residual connections on learning. AAAI Conference on Artificial Intelligence, 31, 02 2016. doi: 10.1609/aaai.v31i1.11231.Domen Tabernik, Samo Šela, Jure Skvarč, and Danijel Skočaj. Segmentation-Based Deep-Learning Approach for Surface-Defect Detection. Journal of Intelligent Manufacturing, May 2019. ISSN 1572-8145. doi: 10.1007/s10845-019-01476-x.Tomoumi Takase, Ryo Karakida, and Hideki Asoh. Self-paced Data Augmentation for Training Neural Networks, 2020.Michael W. Trosset, Carey E. Priebe, Youngser Park, and Michael I. Miller. Semisupervised learning from dissimilarity data. Computational Statistics & Data Analysis, 52(10):4643--4657, 2008. ISSN 0167-9473. doi: 10.1016/j.csda.2008.02.030.Marc van Kreveld, Tillmann Miltzow, Tim Ophelders, Willem Sonke, and Jordi L. Vermeulen. Between shapes, using the Hausdorff distance. Computational Geometry, 100, 2022. ISSN 0925-7721. doi: 10.1016/j.comgeo.2021.101817.Srikanth Vemula and Michael Frye. Mask R-CNN powerline detector: A deep learning approach with applications to a UAV. In 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), pages 1--6, 2020. doi: 10.1109/DASC50938.2020.9256456.R. Venkatesan and Li. Baoxin. Convolutional neural networks in visual computing : a concise guide. CRC press, first edition edition, 2017. ISBN 9781315154282 (ebook). URL https://lccn.loc.gov/2017029154.Eduardo Villegas-Jaramillo. Github-candies, 2023. URL https://github.com/ejvillegasj/candies.Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. Computational Analysis of Multiple Instance Learning-Based Systems for Automatic Visual Inspection: A Doctoral Research Proposal. In Sara Rodríguez, Javier Prieto, Pedro Faria, Sławomir Kłos, Alberto Fernández, Santiago Mazuelas, M. Dolores Jiménez-López, María N. Moreno, and Elena M. Navarro, editors, Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference, pages 374--377, Cham, 2019. Springer International Publishing. ISBN 978-3-319-99608-0. doi: 10.1007/978-3-319-99608-0_49.Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection, pages 67--99. IGI Global, 701 E. Chocolate Avenue Hershey PA, USA 17033, 2022. doi: 10.4018/978-1-6684-4991-2.ch004.Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. Candy classification using convolutional neural networks, data augmentation and transfer learning: Application and a new public dataset. In 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), pages 1--7, 2023a. doi: 10.1109/ICPRS58416.2023.10179072.Eduardo Villegas-Jaramillo and Mauricio Orozco-Alzate. An Inexactly Supervised Methodology Based on Multiple Instance Learning, Convolutional Neural Networks and Dissimilarities for Interpretable Defect Detection and Localization on Textured Surfaces. IEEE Access, 11:138229--138246, 2023b. doi: 10.1109/ACCESS.2023.3340047.Eduardo Villegas-Jaramillo, Ana Lorena Uribe-Hurtado, and Mauricio Orozco-Alzate. Multi-core Parallelization of Point Set Dissimila- rities for Accelerating the Comparison of Bags with Many Instances. In Sigeru Omatu, Rashid Mehmood, Pawel Sitek, Serafino Cicerone, and Sara Rodríguez, editors, Distributed Computing and Artificial Intelligence, 19th International Conference, pages 208--218, Cham, 2023. Springer International Publishing. ISBN 978-3-031-20859-1.A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis. Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 2018:13, 2018. ISSN 1687-5265. doi: 10.1155/2018/7068349.Hua Wang, Cuiqin Ma, and Lijuan Zhou. A Brief Review of Machine Learning and Its Application. In 2009 International Conference on Information Engineering and Computer Science, pages 1--4, 2009. doi: 10.1109/ICIECS.2009.5362936.Juan Wang and Bin Xia. Weakly supervised image segmentation beyond tight bounding box annotations. Computers in Biology and Medicine, 169:107913, 2024. ISSN 0010-4825. doi: 10.1016/j.compbiomed.2023.107913. URL https://www.sciencedirect.com/ science/article/pii/S0010482523013781.Shenlong Wang, Kaixin Han, and Jiafeng Jin. Review of image low-level feature extraction methods for content-based image retrieval. Sensor Review, ahead-of-print, 08 2019a. doi: 10.1108/SR-04-2019-0092.Wei Wang, Linyang He, Guohua Cheng, Ting Wen, and Yan Tian. Learning from ambiguous labels for X-Ray security inspection via weakly supervised correction. Multimedia Tools and Applications, 83:1--16, 05 2023. doi: 10.1007/s11042-023-15299-9.Xinggang Wang, Yongluan Yan, Peng Tang, Wenyu Liu, and Xiaojie Guo. Bag similarity network for deep multi-instance learning. Information Sciences, 504:578--588, 2019b. ISSN 0020-0255. doi: 10.1016/j.ins.2019.07.071.Xin Wen, Jvran Shan, Yu He, and Kechen Song. Steel Surface Defect Recognition: A Survey. Coatings, 13(1), 2023. ISSN 2079-6412. doi: 10.3390/coatings13010017.Xinquan Wu and Xuefeng Yan. A spatial pyramid pooling-based deep reinforcement learning model for dynamic job-shop scheduling problem. Computers & Operations Research, 160:106401, 2023. ISSN 0305-0548. doi: 10.1016/j.cor.2023.106401.Ting Xiao, Lei Liu, Kai Li, Wenjian Qin, Nicolas Yu, and Zhi-Cheng Li. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. BioMed Research International, 2018:1--9, 06 2018. doi: 10.1155/2018/4605191.Xin Xu and Eibe Frank. Logistic regression and boosting for labeled bags of instances. In Honghua Dai, Ramakrishnan Srikant, and Chengqi Zhang, editors, Advances in Knowledge Discovery and Data Mining, pages 272--281, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg. ISBN 978-3-540-24775-3. doi: 10.1007/978-3-540-24775-3_35.Yan Xu, Yeshu Li, Zhengyang Shen, Ziwei Wu, Teng Gao, Yubo Fan, Maode Lai, and Eric Chang. Parallel multiple instance learning for extremely large histopathology image analysis. BMC Bioinformatics, 18, 08 2017. doi: 10.1186/s12859-017-1768-8.Mingqiang Yang, Kidiyo Kpalma, and Joseph Ronsin. A Survey of Shape Feature Extraction Techniques. IN-TECH, 15, 11 2007. doi: 10.5772/6237.Chia-Yu Yen and Krzysztof J. Cios. Image recognition system based on novel measures of image similarity and cluster validity. Neurocomputing, 72(1):401--412, 2008. ISSN 0925-2312. doi: 10.1016/j.neucom.2007.12.018. Machine Learning for Signal Processing (MLSP 2006) / Life System Modelling, Simulation, and Bio-inspired Computing (LSMS 2007).Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, and Antonio Plaza. Optical Remote Sensing Image Understanding With Weak Supervision: Concepts, methods, and perspectives. IEEE Geoscience and Remote Sensing Magazine, 10 (2):250--269, 2022. doi: 10.1109/MGRS.2022.3161377.Amelia Zafra and Eva Gibaja. Nearest neighbor-based approaches for multi-instance multi-label classification. Expert Systems with Applications, 232:120876, 2023. ISSN 0957-4174. doi: 10.1016/j.eswa.2023.120876.Dingwen Zhang, Junwei Han, Gong Cheng, and Ming-Hsuan Yang. Weakly Supervised Object Localization and Detection: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5866--5885, 2022. doi: 10.1109/TPAMI.2021.3074313.W. J. Zhang, D. Li, F. Ye, and H. Sun. Automatic optical defect inspection and dimension measurement of drill bit. In 2006 International Conference on Mechatronics and Automation, pages 95--100, 2006. doi: 10.1109/ICMA.2006.257459.Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning Deep Features for Discriminative Localization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2921--2929, 2016. doi: 10.1109/CVPR.2016.319.Lei Zhou, Yu Zhao, Jie Yang, Qi Yu, and Xun Xu. Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Processing, 12(4):563--571, 2018. ISSN 1751-9659. doi: 10.1049/iet-ipr.2017.0636.Tongxue Zhou, Su Ruan, and Stéphane Canu. A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 3-4:100004, sep 2019. ISSN 2590-0056. doi: 10.1016/j.array.2019.100004.Zhi-Hua Zhou. A Brief Introduction to Weakly Supervised Learning. National Science Review, 1(August 2017):44--53, 2017. ISSN 2095-5138. doi: 10.1093/nsr/nwx106.Keqian Zhu and Jingfei Jiang. Research on Parallel Acceleration for Deep Learning Inference Based on Many-Core ARM Platform. In Chao Li and Junjie Wu, editors, Advanced Computer Architecture, pages 30--41, Singapore, 2018. Springer Singapore. ISBN 978-981-13-2423-9. doi: 10.1007/978-981-13-2423-9_3.Justus Zipfel, Felix Verworner, Marco Fischer, Uwe Wieland, Mathias Kraus, and Patrick Zschech. Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models. Computers & Industrial Engineering, 177: 109045, 2023. ISSN 0360-8352. doi: 10.1016/j.cie.2023.109045.Łukasz Struski, Szymon Janusz, Jacek Tabor, Michał Markiewicz, and Arkadiusz Lewicki. Multiple instance learning for medical image classification based on instance importance. Biomedical Signal Processing and Control, 91:105874, 2024. ISSN 1746-8094. doi: 10.1016/j.bspc.2023.105874. URL https://www.sciencedirect.com/science/article/pii/S1746809423013071BibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalORIGINAL10275156.2024.pdf10275156.2024.pdfTesis de Doctorado en Ingeniería - Industria y Organizacionesapplication/pdf17573154https://repositorio.unal.edu.co/bitstream/unal/86112/2/10275156.2024.pdf24f679c8cde71a6b7639a26dfc589000MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86112/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL10275156.2024.pdf.jpg10275156.2024.pdf.jpgGenerated Thumbnailimage/jpeg4061https://repositorio.unal.edu.co/bitstream/unal/86112/4/10275156.2024.pdf.jpgaccdde226af2c44ffe13a26bf57db8a6MD54unal/86112oai:repositorio.unal.edu.co:unal/861122024-05-17 23:04:36.818Repositorio Institucional Universidad Nacional de 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Metodología heterogénea para la inspección visual automática basada en técnicas de aprendizaje inexactamente supervisado

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