3D biometrics cnn color computer vision content based image retrieval convolutional neural networks deep learning dehazing document search ecg ELECTRIC ARC FURNACE health histological image human-machine interaction hyperspectral image processing industry laser scanning machine learning machine vision neural networks object detection plant disease quality control retinex robots security semantic search surface quality
2018 |
Salado, Juan Pablo; Picón, Artzai; Bereciartua, Arantza; Irusta, Unai Contaje de mitosis en imágenes histológicas mediante redes neuronales convolucionales (Artículo de revista) 2018. (BibTeX | Etiquetas: cnn, mitosis) @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} } |
Picon, Artzai; Alvarez-Gila, Aitor; Seitz, Maximiliam; Ortiz-Barredo, Amaia; Echazarra, Jone; Johannes, Alexander Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild (Artículo de revista) Computers and Electronics in Agriculture, 2018, ISSN: 0168-1699. (Resumen | Enlaces | BibTeX | Etiquetas: cnn, convolutional neural networks, deep learning, disease identification, early pest, image processing, phytopathology, plant disease, precision agriculture) @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/2018/10/preprint_computer_and_electronics_10.1016@j.compag.2018.04.002-1.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. |
2017 |
Alvarez-Gila, Aitor; van de Weijer, Joost; Garrote, Estíbaliz Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB (Artículo en actas) 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 480–490, 2017, (The arxiv.org version contains updated results.). (Resumen | Enlaces | BibTeX | Etiquetas: cnn, color, GAN, hyperspectral, neural networks) @inproceedings{alvarez-gila_adversarial_2017, title = {Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB}, author = {Aitor Alvarez-Gila and Joost van de Weijer and Estíbaliz Garrote}, url = {https://arxiv.org/abs/1709.00265}, doi = {10.1109/ICCVW.2017.64}, year = {2017}, date = {2017-01-01}, booktitle = {2017 IEEE International Conference on Computer Vision Workshops (ICCVW)}, pages = {480--490}, abstract = {Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.}, note = {The arxiv.org version contains updated results.}, keywords = {cnn, color, GAN, hyperspectral, neural networks}, pubstate = {published}, tppubtype = {inproceedings} } Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset. |
Picon, Artzai; Irusta, Unai; Alvarez-Gila, Aitor; Aramendi, Elisabete; Garrote, Estibaliz; Ayala, Unai; Alonso, Felipe; Figuera, Carlos Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo (Artículo en actas) CASEIB 2017 - XXXV Congreso Anual de la Sociedad Espanola de Ingeniería Biomedica, Bilbao, Spain, 2017. (Resumen | Enlaces | BibTeX | Etiquetas: cnn, convolutional neural networks, ecg, fibrillation, LSTM, neural networks, recurrent neural networks, RNN) @inproceedings{picon_deteccion_2017, title = {Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo}, author = {Artzai Picon and Unai Irusta and Aitor Alvarez-Gila and Elisabete Aramendi and Estibaliz Garrote and Unai Ayala and Felipe Alonso and Carlos Figuera}, url = {https://computervision.tecnalia.com/wp-content/uploads/2018/01/caseib_2017_picon.pdf}, year = {2017}, date = {2017-01-01}, booktitle = {CASEIB 2017 - XXXV Congreso Anual de la Sociedad Espanola de Ingeniería Biomedica}, address = {Bilbao, Spain}, abstract = {La detección de arritmias ventriculares, en particular la fibrilación ventricular (FV), es parte fundamental de los algoritmos de clasificación de arritmias de los desfibriladores. Dichos algoritmos deciden si administrar la descarga de desfibrilación, para lo que clasifican los ritmos en desfibrilables (Sh) o no desfibrilables (NSh). Este trabajo propone un nuevo abordaje para la clasificación Sh/NSh de ritmos basado en un sistema de aprendizaje profundo. Para el trabajo se emplearon tres bases de datos públicas de la plataforma Physionet (CUDB, VFDB y AHADB), y se extrajeron segmentos de 4 y 8 segundos. Se anotaron los segmentos como Sh y NSh en base a las anotaciones de las bases de datos, que fueron auditadas por expertos. Los datos se dividieron por paciente en 80% para desarrollar los algoritmos y 20% para evaluación. El sistema de aprendizaje profundo emplea dos etapas convolucionales seguidas de, una red long-short-term-memory y una etapa final de clasificación basada en red neuronal. A modo de referencia se optimizó un clasificador SVM basado en las características de detección de arritmias ventriculares más eficientes publicadas en la literatura. Se calculó la sensibilidad (Se), ritmos desfibrilables, especificidad (Sp), ritmos no desfibrilables, y la precisión (Acc). El método de aprendizaje profundo proporcionó Se, Sp y Acc de 98.5%, 99.4% y 99.2% para segmentos de 4 segundos y 99.7%, 98.9%, 99.1% para segmentos de 8 segundos. El algoritmo permite detectar FV de forma fiable con segmentos de 4 segundos, corrigiendo un 30% de los errores del método basado en SVM.}, keywords = {cnn, convolutional neural networks, ecg, fibrillation, LSTM, neural networks, recurrent neural networks, RNN}, pubstate = {published}, tppubtype = {inproceedings} } La detección de arritmias ventriculares, en particular la fibrilación ventricular (FV), es parte fundamental de los algoritmos de clasificación de arritmias de los desfibriladores. Dichos algoritmos deciden si administrar la descarga de desfibrilación, para lo que clasifican los ritmos en desfibrilables (Sh) o no desfibrilables (NSh). Este trabajo propone un nuevo abordaje para la clasificación Sh/NSh de ritmos basado en un sistema de aprendizaje profundo. Para el trabajo se emplearon tres bases de datos públicas de la plataforma Physionet (CUDB, VFDB y AHADB), y se extrajeron segmentos de 4 y 8 segundos. Se anotaron los segmentos como Sh y NSh en base a las anotaciones de las bases de datos, que fueron auditadas por expertos. Los datos se dividieron por paciente en 80% para desarrollar los algoritmos y 20% para evaluación. El sistema de aprendizaje profundo emplea dos etapas convolucionales seguidas de, una red long-short-term-memory y una etapa final de clasificación basada en red neuronal. A modo de referencia se optimizó un clasificador SVM basado en las características de detección de arritmias ventriculares más eficientes publicadas en la literatura. Se calculó la sensibilidad (Se), ritmos desfibrilables, especificidad (Sp), ritmos no desfibrilables, y la precisión (Acc). El método de aprendizaje profundo proporcionó Se, Sp y Acc de 98.5%, 99.4% y 99.2% para segmentos de 4 segundos y 99.7%, 98.9%, 99.1% para segmentos de 8 segundos. El algoritmo permite detectar FV de forma fiable con segmentos de 4 segundos, corrigiendo un 30% de los errores del método basado en SVM. |
2016 |
Cruz-Lopez, Antonio; Lago, Alberto; Gonzalez, Roberto; Alvarez-Gila, Aitor; Olabarria, Jose Angel Gutiérrez High-speed inspection system finds defects in steel (Artículo de revista) Vision Systems Design, (December 2016 - 1), pp. 24–27, 2016. (Enlaces | BibTeX | Etiquetas: cnn, deep learning, neural networks, surface quality) @article{cruz-lopez_high-speed_2016, title = {High-speed inspection system finds defects in steel}, author = {Antonio Cruz-Lopez and Alberto Lago and Roberto Gonzalez and Aitor Alvarez-Gila and Jose Angel Gutiérrez Olabarria}, url = {http://digital.vision-systems.com/visionsystems/201612?pg=26}, year = {2016}, date = {2016-01-01}, urldate = {2016-12-12}, journal = {Vision Systems Design}, number = {December 2016 - 1}, pages = {24--27}, keywords = {cnn, deep learning, neural networks, surface quality}, pubstate = {published}, tppubtype = {article} } |
Alvarez-Gila, Aitor; Cruz-Lopez, Antonio; Rodriguez-Vaamonde, Sergio; Linares, Miguel; Gutierrez-Olabarria, José A; Garrote, Estibaliz Deep Convolutional Neural Networks for surface quality inspection of hot long metal products (Artículo en actas) First European Machine Vision Forum, Heidelberg, Germany, 2016. (Enlaces | BibTeX | Etiquetas: cnn, convolutional neural networks, deep learning, neural networks, surface quality) @inproceedings{alvarez-gila_deep_2016b, title = {Deep Convolutional Neural Networks for surface quality inspection of hot long metal products}, author = {Aitor Alvarez-Gila and Antonio Cruz-Lopez and Sergio Rodriguez-Vaamonde and Miguel Linares and José A Gutierrez-Olabarria and Estibaliz Garrote}, url = {https://computervision.tecnalia.com/wp-content/uploads/2016/09/EMVA-Deep-Convolutional-Neuronal-Networks-for-surface-quality-inspection-of-hot-long-metal-products.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {First European Machine Vision Forum}, address = {Heidelberg, Germany}, keywords = {cnn, convolutional neural networks, deep learning, neural networks, surface quality}, pubstate = {published}, tppubtype = {inproceedings} } |