Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Clasificación de imágenes"'
Publikováno v:
Ciencia e Ingeniería Neogranadina, Vol 33, Iss 2 (2023)
En el presente estudio se examinó el rendimiento de los algoritmos Support Vector Machine (SVM) y Random Forest (RF) utilizando un modelo de segmentación de imágenes basado en objetos (OBIA) en la zona metropolitana de Barranquilla, Colombia. El p
Externí odkaz:
https://doaj.org/article/c629b9cf63124280949e5f3da8060a03
Publikováno v:
Revista de Investigaciones Marinas, Vol 32, Iss 2 (2023)
Durante los meses de octubre de 2011 y abril de 2012 se llevaron a cabo expediciones al golfo de Ana María para realizar una caracterización del mismo. En dichas expediciones se tomaron como referencia una serie de estaciones de muestreo que sirvie
Externí odkaz:
https://doaj.org/article/411c924afa2e4e1f9cb829ebd630ef9f
Autor:
Néstor Iván Rojas Gamba, Wilson Alfredo Medina Sierra, Liby Angélica Fonseca Salamanca, Giovanna Mayelle Lobaton Piñeros
Publikováno v:
Ciencia e Ingeniería Neogranadina, Vol 31, Iss 2 (2021)
El desarrollo de la ciudad genera problemas cuando las edificaciones no se construyen cumpliendo las normativas necesarias. El objetivo del presente estudio es caracterizar la tipología estructural de edificaciones construidas en el periodo compren
Externí odkaz:
https://doaj.org/article/04ba438e3ff84924a8dd0750cd7fb566
Autor:
Juan F. Heredia-Gómez, Juan P. Rueda-Gómez, Leonardo H. Talero-Sarmiento, Juan S. Ramírez-Acuña, Roberto A. Coronado-Silva
Publikováno v:
Revista Colombiana de Computación, Vol 21, Iss 2 (2020)
Una correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que di
Externí odkaz:
https://doaj.org/article/2fcc1ed9f00f486480ff3cda6ba2baf5
Publikováno v:
Gestión y Ambiente, Vol 21, Iss 1, Pp 41-58 (2018)
La expansión urbana genera impactos ambientales que degradan los ecosistemas naturales, cambiando su estructura y función. En este trabajo se desarrollaron lineamientos metodológicos mediante el uso de herramientas geográficas y teledetección pa
Externí odkaz:
https://doaj.org/article/7e56baafd59d43eca973d88c47a9b3cd
Publikováno v:
Biología Acuática, Iss 33 (2019)
Las lagunas pampeanas evidencian una dinámica muy cambiante (estacional, anual o cíclicamente) en diversos aspectos: profundidad, superficie libre, turbidez, concentración de nutrientes, de clorofila, de sólidos en suspensión, conductividad, etc
Externí odkaz:
https://doaj.org/article/d668337c93a64740b78731f19075754a
Los campos conocidos como Deep Learning y Machine Learning han evolucionado durante años hasta el día de hoy, y lo van a seguir haciendo. Hace unos años, surgieron un nuevo tipo de redes neuronales profundas, llamadas redes neuronales convoluciona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fab85b363de3e5c1aa6f8c08cb8186c
Los campos conocidos como Deep Learning y Machine Learning han evolucionado durante años hasta el día de hoy, y lo van a seguir haciendo. Hace unos años, surgieron un nuevo tipo de redes neuronales profundas, llamadas redes neuronales convoluciona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7af407f3bb8daea445b3a91f75a72eed
Publikováno v:
S. Russel and P. Norvig, ((Artificial Intelligence: A Modern Approach)), Pearson Education Limited, Third Edition, 2016.
Millán Gómez, Sim´on (2006). Procedimientos de Mecanizado. Madrid: Editorial Paraninfo, 19/05/2022.
Oberg, Erik, 1881-; McCauley, Christopher J. (2012). Machinery’s handbook : a reference book for the mechanical engineer, designer, manufacturing engineer, draftsman, toolmaker, and machinist (29th ed edici´on). Industrial Press
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ((ImageNet Classification with Deep Convolutional Neural Networks)), Comunications of the ACM, Vol 60 No 6 p´ags 84-90, 2017
Dan Ciresan, Ueli Meier and Jurguen Schmidhuber, ((Multi-Column Deep Neural Networks for Image Classification ))
A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way, https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neuralnetworks-the-eli5-way-3bd2b1164a53, 19/05/2022
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik, ((Rich Feature hierarchies for accurate object the tension and semantic segmentation)) CVPR. 2014.
Albert Soto, ((YOLO object detector for onboard driving images)) Escola d´Enginyeria Universidad Autónoma de Barcelona.
LeCun, Y., Huang, F., Bottou, L., ((Learning methods for generic object recognition with invariance to pose and lighting)) In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004. Volume 2 (2004). IEEE, II–97.
Ling Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
Griffin, G., Holub, A., Perona, P, ((Caltech-256 object category dataset. )) Technical Report 7694, California Institute of Technology, 2007.
YOLO architecture, https://www.researchgate.net/figure/YOLO-architecture-YOLOarchitecture-is-inspired-by-GooLeNet-model-for-image fig2 329038564, 20/05/2022.
Fei-Fei, L., Fergus, R., Perona, P., ((Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories)), Comput. Vision Image Understanding 106, 1 (2007), 59–70.
Krizhevsky, A, ((Learning multiple layers of features from tiny images)). Master’s thesis, Department of Computer Science, University of Toronto, 2009
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., ((Going deeper with convolutions)) 2014
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li and Li Fei-Fei ((ImageNet: A large-scale hierarchical image database)). 2009. Conference on computer vision
Hyndman, Rob J.; Koehler, Anne B. (2006). .Another look at measures of forecast accuracy”. International Journal of Forecasting.
Jaccard index, https://www.researchgate.net/publication/239604848 The Probabilistic Basis of Jaccard%23/05/2022.
Tom Fawcett, An introduction to ROC analysis, Institute for the Study of Learning and Expertise, (2005) https://people.inf.elte.hu/kiss/11dwhdm/roc.pdf23/05/2022.
Powers David, Evaluation: From Precision, Recall and FFactor to ROC, Informedness, Markedness & Correlation 2007, https://web.archive.org/web/20191114213255/https://www.flinders.edu.au/science engineering/fms/School-CSEM/publications/tech repsresearch artfcts/TRRA 2007.pdf 19/11/2022.
Information Retrieval, https://en.wikipedia.org/w/index.php?title=Information retrievaldirection=next&19/11/2022
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). The Elements of Statistical Learning.
Cross Entropy, The Mathematics of Information Coding, Extraction and Distribution, by George Cybenko, Dianne P. O’Leary, Jorma Rissanen, 1999.
Shapiro, L. G. & Stockman, G. C: C¸ omputer Vision”, page 137, 150. Prentice Hall, 2001.
Vaswani A., Shazeer N., Parmar N., Uskoreit J., Jones L., Gomez A., Lukasz K., Polosukhin I. ((Attention Is All You Need )) 31st Conference on Neural Information Processing Systems. 2017. Long Beach, CA, USA.
Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X, Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (( Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale)) . 2021. Google Research, Brain team.
He K., Zhang X., Ren S., Sun J. (( Deep Residual Learning fro image Recognition )) . 2015. Microsoft Research.
Lui Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. ((A ConvNet for the 2020s )). 2022. Facebook AI Research
Ulrich M., Follmann P., Neudeck J. ((A comparison of shape-based matching with deep-learning-based object detection)) Technisches Messen. 2019. DOI 10.1515/teme-2019-0076.
Ren, Shaoqing He, Kaiming Girshick, Ross Sun, Jian. ((Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI 10.1109/TPAMI.2016.2577031.
Detection Evaluation, https://cocodataset.org/home, 19/11/2022.
Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh. ((MobileNetV2: Inverted Residuals and Linear Bottlenecks)), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
Mudumbi, Terry Bian, Naizheng Zhang, Yiyi Hazoume, Florian. ((An Approach Combined the Faster RCNN and Mobilenet for Logo Detection)), Journal of Physics: Conference Series. 2019. 10.1088/1742-6596/1284/1/012072.
Srivastava, Animesh and Dalvi, Anuj and Britto, Cyrus and Rai, Harshit and Shelke, Kavita ((Explicit Content Detection using Faster R-CNN and SSD MobileNet v2.)) 2020. Int. Res. J. Eng. Technol, 7, 5572–5577.
Tan L, Huangfu T, Wu L, Chen W. ((Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification)) Research Square. 2021. DOI: 10.21203/rs.3.rs-668895/v1.
Ahmed, Khaled R. ((Smart Pothole Detection Using Deep Learning Based on Dilated Convolution)) Sensors. 2021. DOI: 10.3390/s21248406.
Girshick, Ross. ((Fast R-CNN)). Proceedings of the IEEE international conference on computer vision. 2015. p. 1440-1448.
Karim, Shahid Zhang, Ye Yin, Shoulin Bibi, Irfana Brohi, Ali. ((A brief review and challenges of object detection in optical remote sensing imagery. Multiagent and Grid Systems)) 2020. DOI: 10.3233/MGS-200330.
Short-Term Load Forecasting based on ResNet and LSTM-Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/The-structure-of-ResNet-12 fig1 329954455 [accessed 23 Nov, 2022]
Everingham, M., Eslami, S., Gool, L. V., Williams, C., Winn, J., Zisserman, A. ((The pascal visual object classes challenge: A retrospective )) IJCV. 2015.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ((ImageNet large scale visual recognition challenge)) IJCV. 2015.
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., Zitnick, L. ((Microsoft COCO: Common objects in context)) In ECCV. 2015.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A. ((Places: A 10 million image database for scene recognition)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., PontTuset, J., et al. ((The open images dataset v4: Unified image classi-fication, object detection, and visual relationship detection at scale)) arXiv:1811.00982. 2018.
Li, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R. ((Benchmarking detection transfer learning with vision transformers)) arXiv preprint arXiv:2111.11429. 2021.
Liu, L., Ouyang, W., Wang, X. et al. ((Deep Learning for Generic Object Detection: A Survey)) Int J Comput Vis. 2020. https://doi.org/10.1007/s11263-019-01247-4
Chuanqi T., Fuchun S., Tao K.,Wenchang Z., Chao Y. Chunfang L.(( A Survey on Deep Transfer Learning)) The 27th International Conference on Artificial Neural Networks. 2018
Ze L., Yutong L., Yue C., Han H., Yixuan W., Zheng Z., Stephen L. Baining G.((Swin Transformer: Hierarchical Vision Transformer using Shifted Windows)) 2021
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Millán Gómez, Sim´on (2006). Procedimientos de Mecanizado. Madrid: Editorial Paraninfo, 19/05/2022.
Oberg, Erik, 1881-; McCauley, Christopher J. (2012). Machinery’s handbook : a reference book for the mechanical engineer, designer, manufacturing engineer, draftsman, toolmaker, and machinist (29th ed edici´on). Industrial Press
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ((ImageNet Classification with Deep Convolutional Neural Networks)), Comunications of the ACM, Vol 60 No 6 p´ags 84-90, 2017
Dan Ciresan, Ueli Meier and Jurguen Schmidhuber, ((Multi-Column Deep Neural Networks for Image Classification ))
A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way, https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neuralnetworks-the-eli5-way-3bd2b1164a53, 19/05/2022
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik, ((Rich Feature hierarchies for accurate object the tension and semantic segmentation)) CVPR. 2014.
Albert Soto, ((YOLO object detector for onboard driving images)) Escola d´Enginyeria Universidad Autónoma de Barcelona.
LeCun, Y., Huang, F., Bottou, L., ((Learning methods for generic object recognition with invariance to pose and lighting)) In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004. Volume 2 (2004). IEEE, II–97.
Ling Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
Griffin, G., Holub, A., Perona, P, ((Caltech-256 object category dataset. )) Technical Report 7694, California Institute of Technology, 2007.
YOLO architecture, https://www.researchgate.net/figure/YOLO-architecture-YOLOarchitecture-is-inspired-by-GooLeNet-model-for-image fig2 329038564, 20/05/2022.
Fei-Fei, L., Fergus, R., Perona, P., ((Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories)), Comput. Vision Image Understanding 106, 1 (2007), 59–70.
Krizhevsky, A, ((Learning multiple layers of features from tiny images)). Master’s thesis, Department of Computer Science, University of Toronto, 2009
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., ((Going deeper with convolutions)) 2014
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li and Li Fei-Fei ((ImageNet: A large-scale hierarchical image database)). 2009. Conference on computer vision
Hyndman, Rob J.; Koehler, Anne B. (2006). .Another look at measures of forecast accuracy”. International Journal of Forecasting.
Jaccard index, https://www.researchgate.net/publication/239604848 The Probabilistic Basis of Jaccard%23/05/2022.
Tom Fawcett, An introduction to ROC analysis, Institute for the Study of Learning and Expertise, (2005) https://people.inf.elte.hu/kiss/11dwhdm/roc.pdf23/05/2022.
Powers David, Evaluation: From Precision, Recall and FFactor to ROC, Informedness, Markedness & Correlation 2007, https://web.archive.org/web/20191114213255/https://www.flinders.edu.au/science engineering/fms/School-CSEM/publications/tech repsresearch artfcts/TRRA 2007.pdf 19/11/2022.
Information Retrieval, https://en.wikipedia.org/w/index.php?title=Information retrievaldirection=next&19/11/2022
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). The Elements of Statistical Learning.
Cross Entropy, The Mathematics of Information Coding, Extraction and Distribution, by George Cybenko, Dianne P. O’Leary, Jorma Rissanen, 1999.
Shapiro, L. G. & Stockman, G. C: C¸ omputer Vision”, page 137, 150. Prentice Hall, 2001.
Vaswani A., Shazeer N., Parmar N., Uskoreit J., Jones L., Gomez A., Lukasz K., Polosukhin I. ((Attention Is All You Need )) 31st Conference on Neural Information Processing Systems. 2017. Long Beach, CA, USA.
Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X, Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (( Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale)) . 2021. Google Research, Brain team.
He K., Zhang X., Ren S., Sun J. (( Deep Residual Learning fro image Recognition )) . 2015. Microsoft Research.
Lui Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. ((A ConvNet for the 2020s )). 2022. Facebook AI Research
Ulrich M., Follmann P., Neudeck J. ((A comparison of shape-based matching with deep-learning-based object detection)) Technisches Messen. 2019. DOI 10.1515/teme-2019-0076.
Ren, Shaoqing He, Kaiming Girshick, Ross Sun, Jian. ((Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI 10.1109/TPAMI.2016.2577031.
Detection Evaluation, https://cocodataset.org/home, 19/11/2022.
Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh. ((MobileNetV2: Inverted Residuals and Linear Bottlenecks)), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
Mudumbi, Terry Bian, Naizheng Zhang, Yiyi Hazoume, Florian. ((An Approach Combined the Faster RCNN and Mobilenet for Logo Detection)), Journal of Physics: Conference Series. 2019. 10.1088/1742-6596/1284/1/012072.
Srivastava, Animesh and Dalvi, Anuj and Britto, Cyrus and Rai, Harshit and Shelke, Kavita ((Explicit Content Detection using Faster R-CNN and SSD MobileNet v2.)) 2020. Int. Res. J. Eng. Technol, 7, 5572–5577.
Tan L, Huangfu T, Wu L, Chen W. ((Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification)) Research Square. 2021. DOI: 10.21203/rs.3.rs-668895/v1.
Ahmed, Khaled R. ((Smart Pothole Detection Using Deep Learning Based on Dilated Convolution)) Sensors. 2021. DOI: 10.3390/s21248406.
Girshick, Ross. ((Fast R-CNN)). Proceedings of the IEEE international conference on computer vision. 2015. p. 1440-1448.
Karim, Shahid Zhang, Ye Yin, Shoulin Bibi, Irfana Brohi, Ali. ((A brief review and challenges of object detection in optical remote sensing imagery. Multiagent and Grid Systems)) 2020. DOI: 10.3233/MGS-200330.
Short-Term Load Forecasting based on ResNet and LSTM-Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/The-structure-of-ResNet-12 fig1 329954455 [accessed 23 Nov, 2022]
Everingham, M., Eslami, S., Gool, L. V., Williams, C., Winn, J., Zisserman, A. ((The pascal visual object classes challenge: A retrospective )) IJCV. 2015.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ((ImageNet large scale visual recognition challenge)) IJCV. 2015.
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., Zitnick, L. ((Microsoft COCO: Common objects in context)) In ECCV. 2015.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A. ((Places: A 10 million image database for scene recognition)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., PontTuset, J., et al. ((The open images dataset v4: Unified image classi-fication, object detection, and visual relationship detection at scale)) arXiv:1811.00982. 2018.
Li, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R. ((Benchmarking detection transfer learning with vision transformers)) arXiv preprint arXiv:2111.11429. 2021.
Liu, L., Ouyang, W., Wang, X. et al. ((Deep Learning for Generic Object Detection: A Survey)) Int J Comput Vis. 2020. https://doi.org/10.1007/s11263-019-01247-4
Chuanqi T., Fuchun S., Tao K.,Wenchang Z., Chao Y. Chunfang L.(( A Survey on Deep Transfer Learning)) The 27th International Conference on Artificial Neural Networks. 2018
Ze L., Yutong L., Yue C., Han H., Yixuan W., Zheng Z., Stephen L. Baining G.((Swin Transformer: Hierarchical Vision Transformer using Shifted Windows)) 2021
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
La tarea de clasificación de tornillos hasta el momento es solo ejecutada por humanos. De hecho, las fotos no son aceptadas como insumo para la clasificación de tornillos debido a que existe información que no se puede determinar con las imágenes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c34106a5c58b795de12e91760a924e44
https://repository.urosario.edu.co/handle/10336/38281
https://repository.urosario.edu.co/handle/10336/38281
Autor:
Restrepo-Arias, Juan F.
ilustraciones, diagrama La mayor parte de las variables que se miden en un cultivo agrícola solo pueden ser detectadas de manera visual, por ejemplo, el inventario de plantas y frutos, el desarrollo y las etapas fenológicas de un cultivo o la prese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1326::9a4c6710d7b2dc33d4d628a31669610b
https://repositorio.unal.edu.co/handle/unal/83849
https://repositorio.unal.edu.co/handle/unal/83849