Ernest Valveny, & B. Lamiroy. (2002). Automatic Generation of Browsable Technical Documents..
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C. Mariño, V.M. Gulias, M.G. Penas, M. Penedo, Victor Leboran, A. Mosquera, et al. (2001). Sistema de Interpretacion Automatica de Secuencias solo Basado en un Servidor vod..
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C. Mariño, M.G. Penas, M. Penedo, David Lloret, & M.J. Carreira. (2001). Integration of Mutual Information and Creaseness Based Methods for the Automatic Registration of SLO Sequences..
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Ernest Valveny, & Antonio Lopez. (2003). Numeral Recognition for Quality Control of Surgical Sachets.
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Oriol Ramos Terrades, & Ernest Valveny. (2003). Radon Transform for Lineal Symbol Representation.
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Karla Lizbeth Caballero, Joel Barajas, & Oriol Pujol. (2007). Reconstructing IVUS Images for an Accurate Tissue Classification. In Proceedings of the Second International Conference on Computer Vision Theory and Applications (Vol. Special Sessions, 113–119).
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Ricardo Toledo, Ramon Baldrich, Ernest Valveny, & Petia Radeva. (2002). Enhancing snakes for vessel detection in angiography images..
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M. Bressan, & Jordi Vitria. (2002). Independent Component Analysis and Naïve Bayes Classification. Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501., .
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Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, & Joan Serrat. (2021). Monitoring war destruction from space using machine learning. PNAS - Proceedings of the National Academy of Sciences of the United States of America, 118(23), e2025400118.
Abstract: Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
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David Lloret, C. Mariño, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2001). Landmark-based registration of full SLO video sequences..
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Gemma Sanchez, Josep Llados, & K. Tombre. (2001). An Algorithm to Recognize Graphical Textured Symbols using String Representations..
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David Guillamet, & Jordi Vitria. (2001). Unsupervised Learning of Structural Object Representations.
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Jordi Gonzalez, X. Varona, Juan J. Villanueva, & Xavier Roca. (2001). On-line Human Activity Recognition for Video Surveillance..
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Lluis Barcelo, & X. Binefa. (2001). Bayesian Video Mosaicing with Moving Objects..
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J.M. Sanchez, & X. Binefa. (2001). Semantics from motion in news videos..
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