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Doctor Carlos Moreno-Garcia


Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., DANG, T., MARTIN, K., PATEL, M., THOMPSON, A., LEISHMAN, L. and WIRATUNGA, N. 2020. Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. In Bach, K., Bunescu, R., Marling, C. and Wiratunga, N. (eds.) Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual conference]. CEUR workshop proceedings, 2675. Aachen: CEUR-WS [online], pages 63-70. Available from: http://ceur-ws.org/Vol-2675/paper10.pdf

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the... Read More about Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection..

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. (2020)
Journal Article
ELYAN, E., MORENO-GARCIA, C.F. and JAYNE, C. 2020. CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural computing and applications [online], Online First. Available from: https://doi.org/10.1007/s00521-020-05130-z

Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets re... Read More about CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification..

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. (2020)
Conference Proceeding
ELYAN, E., MORENO-GARCÍA, C.F. and JOHNSTON, P. 2020. Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 215-224. Available from: https://doi.org/10.1007/978-3-030-48791-1_16

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents... Read More about Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks..

Reducing human effort in engineering drawing validation. (2020)
Journal Article
RICA, E., MORENO-GARCÍA, C.F., ÁLVAREZ, S. and SERRATOS, F. 2020. Reducing human effort in engineering drawing validation. Computers in industry [online], 117, article ID 103198. Available from: https://doi.org/10.1016/j.compind.2020.103198

Oil & Gas facilities are extremely huge and have complex industrial structures that are documented using thousands of printed sheets. During the last years, it has been a tendency to migrate these paper sheets towards a digital environment, with the... Read More about Reducing human effort in engineering drawing validation..

A comparison of feature extractors for panorama stitching in an autonomous car architecture. (2019)
Conference Proceeding
CORTÉS-GALLARDO, E., MORENO-GARCIA, C.F., ZHU, A., CHÍPULI-SILVA, D., GONZÁLEZ-GONZÁLEZ, J.A., MORALES-ORTIZ, D., FERNÁNDEZ, S., URRIZA, B., VALVERDE-LÓPEZ, J., MARÍN, A., PÉREZ, H., IZQUIERDO-REYES, J. and BUSTAMANTE-BELLO, R. 2019. A comparison of feature extractors for panorama stitching in an autonomous care architecture. In Proceedings of 2019 International conference on mechatronics, electronics and automotive engineering (ICMEAE 2019), 26-29 November 2019, Cuernavaca, Mexico. Piscataway: IEEE [online], page 50-55. Available from: https://doi.org/10.1109/ICMEAE.2019.00017

Panorama stitching consists on frames being put together to create a 360o view. This technique is proposed for its implementation in autonomous vehicles instead of the use of an external 360o camera, mostly due to its reduced cost and improved aerody... Read More about A comparison of feature extractors for panorama stitching in an autonomous car architecture..

Digitisation of assets from the oil and gas industry: challenges and opportunities. (2019)
Conference Proceeding
MORENO-GARCIA, C.F. and ELYAN, E. 2019. Digitisation of assets from the oil and gas industry: challenges and opportunities. In Proceedings of 2019 International conference on document analysis and recognition workshops (ICDARW), 22-25 September 2019, Sydney, Australia. Piscataway: IEEE [online], 7, pages 2-5. Available from: https://doi.org/10.1109/ICDARW.2019.60122

Automated processing and analysis of legacies of printed documents across the Oil & Gas industry provide a unique opportunity and at the same time pose a significant challenge. One particular example is the case of Piping and Instrumentation Diagrams... Read More about Digitisation of assets from the oil and gas industry: challenges and opportunities..

Generalised median of graph correspondences. (2019)
Journal Article
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2019. Generalised median of graph correspondences. Pattern recognition letters [online], 125, pages 389-395. Available from: https://doi.org/10.1016/j.patrec.2019.05.015

A graph correspondence is defined as a function that maps the elements of two attributed graphs. Due to the increasing availability of methods to perform graph matching, numerous graph correspondences can be deducted for a pair of attributed graphs.... Read More about Generalised median of graph correspondences..

Correspondence edit distance to obtain a set of weighted means of graph correspondences. (2018)
Journal Article
MORENO-GARCÍA, C.F., SERRATOSA, F. and XIAOYI, J. 2020. Correspondence edit distance to obtain a set of weighted means of graph correspondences. Pattern recognition letters [online], 134, pages 29-36. Available from: https://doi.org/10.1016/j.patrec.2018.08.027

Given a pair of data structures, such as strings, trees, graphs or sets of points, several correspondences (also referred in literature as labellings, matchings or assignments) can be defined between their local parts. The Hamming distance has been l... Read More about Correspondence edit distance to obtain a set of weighted means of graph correspondences..

Modelling the generalised median correspondence through an edit distance. (2018)
Conference Proceeding
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2018. Modelling the generalised median correspondence through an edit distance. In Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B. and Robles-Kelly, A. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2018 Joint International Association for Pattern Recognition (IAPR) international workshops on structural and syntactic pattern recognition (SSPR 2018), and statistical techniques in pattern recognition (SPR 2018) (S+SSPR 2018), 17-19 August 2018, Beijing, China. Lecture notes in computer science, 11004. Cham: Springer [online], pages 271-281. Available from: https://doi.org/10.1007/978-3-319-97785-0_26

On the one hand, classification applications modelled by structural pattern recognition, in which elements are represented as strings, trees or graphs, have been used for the last thirty years. In these models, structural distances are modelled as th... Read More about Modelling the generalised median correspondence through an edit distance..

Symbols classification in engineering drawings. (2018)
Conference Proceeding
ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from: https://doi.org/10.1109/IJCNN.2018.8489087

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we p... Read More about Symbols classification in engineering drawings..

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