@inproceedings{ancuti_ntire_2018,
title = {NTIRE 2018 Challenge on Image Dehazing: Methods and Results},
author = {Cosmin Ancuti and Codruta O Ancuti and Radu Timofte and Aitor Alvarez-Gila and Adrian Galdran and Alessandro Bria and Javier Vazquez-Corral and Marcelo Bertalmio and Others},
url = {http://openaccess.thecvf.com/content_cvpr_2018_workshops/w13/html/Ancuti_NTIRE_2018_Challenge_CVPR_2018_paper.html},
year = {2018},
date = {2018-06-01},
urldate = {2018-06-07},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
pages = {891--901},
keywords = {dehazing, retinex},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{salado2018contaje,
title = {Contaje de mitosis en imágenes histológicas mediante redes neuronales convolucionales},
author = {Juan Pablo Salado and Artzai Picón and Arantza Bereciartua and Unai Irusta},
year = {2018},
date = {2018-01-01},
publisher = {Servicio Editorial de la Universidad del Pa'is Vasco/Euskal Herriko Unibertsitatearen Argitalpen Zerbitzua},
keywords = {cnn, mitosis},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{galdran_duality_2018,
title = {On the Duality Between Retinex and Image Dehazing},
author = {Adrian Galdran and Aitor Alvarez-Gila and Alessandro Bria and Javier Vazquez-Corral and Marcelo Bertalmío},
url = {http://openaccess.thecvf.com/content_cvpr_2018/html/Galdran_On_the_Duality_CVPR_2018_paper.html},
year = {2018},
date = {2018-01-01},
urldate = {2018-06-06},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages = {8212--8221},
address = {Salt Lake City, USA},
keywords = {dehazing, retinex},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{picon_deep_2018,
title = {Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild},
author = {Artzai Picon and Aitor Alvarez-Gila and Maximiliam Seitz and Amaia Ortiz-Barredo and Jone Echazarra and Alexander Johannes},
url = {http://www.sciencedirect.com/science/article/pii/S0168169917312619
https://computervision.tecnalia.com/wp-content/uploads/2018/06/poster_tecnalia_emva_forum_A0_v1.0.pdf},
doi = {10.1016/j.compag.2018.04.002},
issn = {0168-1699},
year = {2018},
date = {2018-01-01},
urldate = {2018-06-26},
journal = {Computers and Electronics in Agriculture},
abstract = {Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014,2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.},
keywords = {cnn, convolutional neural networks, deep learning, disease identification, early pest, image processing, phytopathology, plant disease, precision agriculture},
pubstate = {published},
tppubtype = {article}
}
Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014,2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.
@article{rojo2017ladle,
title = {Ladle furnace slag characterization through hyperspectral reflectance regression model for secondary metallurgy process optimization},
author = {Asier Vicente and Artzai Picon and Sergio Rodríguez-Vaamonde and Jose Antonio Arteche and Jorge Armentia and Inaki Macaya},
url = {https://www.researchgate.net/publication/320395826_Fast_method_for_slag_characterization_during_ladle_furnace_steelmaking_process_based_on_spectral_reflectance},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Industrial Informatics},
publisher = {IEEE},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
@article{vicente2017fast,
title = {Fast method for slag characterization during ladle furnace steelmaking process based on spectral reflectance},
author = {Asier Vicente and Iñaki Macaya and Artzai Picon and Jose Antonio Arteche},
url = {https://www.researchgate.net/publication/320395826_Fast_method_for_slag_characterization_during_ladle_furnace_steelmaking_process_based_on_spectral_reflectance},
year = {2017},
date = {2017-01-01},
keywords = {industry},
pubstate = {published},
tppubtype = {article}
}
@article{johannes2017automatic,
title = {Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case},
author = {Alexander Johannes and Artzai Picon and Aitor Alvarez-Gila and Jone Echazarra and Sergio Rodríguez-Vaamonde and Ana Díez Navajas and Amaia Ortiz-Barredo},
url = {https://www.sciencedirect.com/science/article/pii/S016816991631050X},
year = {2017},
date = {2017-01-01},
journal = {Computers and Electronics in Agriculture},
volume = {138},
pages = {200--209},
publisher = {Elsevier},
keywords = {plant disease},
pubstate = {published},
tppubtype = {article}
}
@article{galdran2017data,
title = {Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis},
author = {Adrian Galdran and Aitor Alvarez-Gila and Maria Ines Meyer and Cristina L Saratxaga and Teresa Araújo and Estibaliz Garrote and Guilherme Aresta and Pedro Costa and Ana Maria Mendonça and Aurélio Campilho},
url = {https://arxiv.org/abs/1703.03702},
year = {2017},
date = {2017-01-01},
journal = {arXiv preprint arXiv:1703.03702},
keywords = {health},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{picon_high_2017,
title = {High speed quality inspection of hot long metal products surface based on Deep Convolutional Neural Networks},
author = {Artzai Picon and Aitor Alvarez-Gila and Gorka Duro and Antonio Cruz-Lopez and Miguel Linares and Alberto Lago and Estibaliz Garrote and Gutierrez Olabarria, Jose Angel},
url = {https://computervision.tecnalia.com/wp-content/uploads/2017/06/Surfin_DeepLearn_Bilbao2017.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Open session - International Summer School on Deep Learning 2017},
address = {Bilbao, Spain},
keywords = {deep learning, industry},
pubstate = {published},
tppubtype = {inproceedings}
}
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