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Author |
Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa |
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Title |
A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. |
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2022 |
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Journal of Manufacturing Systems |
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Vol. 64 |
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pp 497 – 507 |
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cidis @ cidis @ |
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184 |
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Author |
Pereira J., Mora M. & W. Agila |
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Title |
Qualitative Model to Maximize Shrimp Growth at Low Cost |
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2021 |
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5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 |
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cidis @ cidis @ |
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167 |
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Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
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Title |
LDC: Lightweight Dense CNN for Edge Detection. |
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2022 |
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IEEE Access journal |
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Vol. 10 |
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pp 68281 – 68290 |
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cidis @ cidis @ |
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183 |
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Author |
Ulises Gildardo Quiroz Antúnez, Alejandro Ismael Monterroso Rivas, María Fernanda Calderón Vega, Adán Guillermo Ramírez García |
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Title |
APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE |
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2022 |
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Granja |
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36 |
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2 |
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cidis @ cidis @ |
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200 |
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Author |
Armin Mehri; Parichehr Behjati; Angel Domingo Sappa |
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Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. |
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2023 |
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IEEE Access |
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Vol. 11 |
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pp. 11529 - 11540 |
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cidis @ cidis @ |
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207 |
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Author |
Boris Vintimilla, Jorge Vulgarin, Henry Velesaca |
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Title |
Deep Learning-based Human Height Estimation from a Stereo Vision System |
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2023 |
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accepted in IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7 |
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cidis @ cidis @ |
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215 |
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Author |
Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia |
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2023 |
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Dense extreme inception network for edge detection. Pattern Recognition, Vol. 139, 109461 |
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cidis @ cidis @ |
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216 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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2018 |
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In Sensors 2018 |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Title |
Fine-tuning deep convolutional networks for lepidopterous genus recognition |
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2017 |
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Lecture Notes in Computer Science |
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gtsi @ user @ |
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63 |
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Author |
Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
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2017 |
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In Sensors Journal |
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17 |
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873 |
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gtsi @ user @ |
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64 |
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