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Giró-i-Nieto X, Marqués F, Casas J. The edition of the Wikipedia as an academic activity. In 4rt. Congrés Internacional de Docència Unversitària i Innovació. Barcelona, Catalonia; 2006. p. –.
Gasull A, Sayrol E, Moreno A, Vallverdu F, Salavedra J, Oliveras A. Editor gráfico de figuras MATLAB. In III Congreso de Usuarios de MATLAB. 1999. pp. 219–227.
Casas J, Torres L, Jareño M. Efficient coding of residual images. In SPIE Visual Communications '93. Cambridge, MA: SPIE; 1993. pp. 694–705.
Bellot P, Meyer PE. Efficient combination of pairwise feature networks. JMLR: Workshop and Conference Proceedings . Nancy, France: JMLR: Workshop and Conference Proceedings; 2014;46:77 - 84.  (217.39 KB)
Bellot P, Meyer P. Efficient Combination of Pairwise Feature Networks. In: Battaglia D, Guyon I, Lemaire V, Orlandi J, Ray B, Soriano J. Neural Connectomics Challenge. Springer International Publishing; 2017.
Bellver M. Efficient Exploration of Region Hierarchies for Semantic Segmentation. Ventura C, Giró-i-Nieto X. 2015.  (11.62 MB)
Bonafonte A, Mariño J, Pardàs M. Efficient integration of coarticulation and lexical information .. In INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING. 1992. pp. 45–48.
Bellver M, Giró-i-Nieto X, Marqués F. Efficient search of objects in images using deep reinforcement learning. NIPS Women in Machine Learning Workshop. Barcelona.; 2016.
Iregui M. Efficient strategies for navigation through very large JPEG2000 image. Marqués F, Macq B. Université Catholique de Louvain (UCL); 2008.
Torres L, Casas J, Campins J. An efficient technique of texture representation in segmentation-based image coding schemes. In IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING. 1995. pp. 588–591.
Carné-Herrera M, Giró-i-Nieto X, Gurrin C. EgoMemNet: Visual Memorability Adaptation to Egocentric Images. Las Vegas, NV, USA: 4th Workshop on Egocentric (First-Person) Vision, CVPR 2016; 2016 .  (265.4 KB)
Chertó M. EgoMon Gaze and Video Dataset for Visual Saliency Prediction. Gurrin C, Giró-i-Nieto X. 2016.  (1.48 MB)
Marqués F, Jofre L, Sole F, Sabate F, Berenguer J, Romeu J, et al.. El concepto NetCampus. In 3es Jornadas de la Cátedra Telefónica-UPC. 2005. pp. 15–20.
Jofre L, Sole F, Sabate F, Berenguer J, Marqués F, Romeu J, et al.. El "Espacio Innovador" y la red. 2005.
Jofre L, Sole F, Sabate F, Marqués F, Romeu J, Torres J. El 'Profesional Innovador' y la red. 2004.
Antoja-Sabin J. El telèfon mòbil com a eina d'aprenentatge informal. Giró-i-Nieto X. 2013.  (1.55 MB)
Gasull A, Torres L. Eleccion de componentes principales para la clasific. no supervisada de image. In IV Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes. 1990. pp. 9–16.
Oliveras A, Espel-Masferrer E. Elevated basal hepcidin levels in the liver may inhibit the development of malaria infection: Another piece towards solving the malaria puzzle?. Medical hypotheses. 2008;70:630–634.
Pardàs M, Bonafonte A, Landabaso J-L. Emotion recognition based on MPEG-4 facial animation parameters. In IEEE International Conference on Acoustics, Speech, and Signal Processing. 2002. pp. 3624–3627.
Pan J, Giró-i-Nieto X. End-to-end Convolutional Network for Saliency Prediction. Large-scale Scene Understanding Challenge (LSUN) at CVPR Workshops . Boston, MA (USA): arXiv; 2015 .  (1.18 MB)
Moreno D, Costa-jussà MR, Giró-i-Nieto X. English to ASL Translator for Speech2Signs. 2018.  (1.54 MB)
Lobo A, Bas P, Marqués F. Enhanced audio data hiding synchronization using non-linear filter. In International Conference on Acoustics, Speech, and Signal Processing. 2004. pp. 885–888.
Gallego J, Pardàs M. Enhanced bayesian foreground segmentation using brightness and color distortion region-based model for shadow removal. In 2010 IEEE International Conference on Image Processing. 2010. pp. 3449–3452.
Gallego J, Pardàs M, Haro G. Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling. Pattern Recognition Letters. 2012;33(12):1558–1568.
Anglada D, Sala J, Marqués F, Salembier P, Pardàs M. Enhancing Ki-67 Cell Segmentation with Dual U-Net Models: A Step Towards Uncertainty-Informed Active Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE; 2024.  (838.72 KB)

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