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2019
Górriz M, Antony J, McGuinness K, Giró-i-Nieto X, O'Connor N. Assessing Knee OA Severity with CNN attention-based end-to-end architectures. In International Conference on Medical Imaging with Deep Learning (MIDL) 2019. London, United Kingdom: JMLR; 2019.  (3.1 MB)
López-Palma M, Morros JR, Corbalán M, Gago J. Audience measurement using a top-view camera and oriented trajectories. In IEEE IECON 2019. Lisbon, Portugal; 2019.  (523.13 KB)
Wang L, Nie D, Li G, Puybareau E, Dolz J, Zhang Q, et al.. Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE Transactions on Medical Imaging. 2019;.
Salvador A. Computer Vision beyond the visible: Image understanding through language. Giró-i-Nieto X, Marqués F. Signal Theory and Communications. [Barcelona]: Universitat Politecnica de Catalunya; 2019.
Roisman A, Navarro A, Clot G, Castellano G, Gonzalez-Farre B, Pérez-Galan P, et al.. Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas. British Journal of Haematology. 2019;184(3):373-383.
Roisman A, Navarro A, Clot G, Castellano G, Gonzalez-Farre B, Pérez-Galan P, et al.. Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas. British Journal of Haematology. 2019;184(3):373-383.
Combalia M, Pérez-Anker J, García-Herrera A, Alos L, Vilaplana V, Marques F, et al.. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
Combalia M, Pérez-Anker J, García-Herrera A, Alos L, Vilaplana V, Marques F, et al.. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
Gené-Mola J, Gregorio E, Guevara J, Cheein FAuat, Sanz R, Escolà A, et al.. Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner. Biosystems Engineering. 2019;187.
Caselles P. Integrating low-level motion cues in deep video saliency. McGuinness K, Giró-i-Nieto X. 2019.  (10.04 MB)
Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data. Data in Brief. 2019;.  (2.43 MB)
Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. Multi-modal Deep Learning for Fuji Apple Detection Using RGB-D Cameras and their Radiometric Capabilities. Computers and Electronics in Agriculture. 2019;162.
Ventura C, Varas D, Vilaplana V, Giró-i-Nieto X, Marques F. Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;.  (4.35 MB)
Mas I, Morros JR, Vilaplana V. Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning. In ICCV Workshop - MDALC 2019. Seoul, South Korea; 2019.  (911.15 KB)
Mas I, Morros JR, Vilaplana V. Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning. In ICCV Workshop - MDALC 2019. Seoul, South Korea; 2019.  (911.15 KB)
Mosella-Montoro A, Ruiz-Hidalgo J. Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification. In IEEE Conference on Computer Vision Workshop (ICCVW). Seoul, Korea: IEEE; 2019.  (314.43 KB)
Ventura C, Bellver M, Girbau A, Salvador A, Marqués F, Giró-i-Nieto X. RVOS: End-to-End Recurrent Network for Video Object Segmentation. In CVPR. Long Beach, CA, USA: OpenCVF / IEEE; 2019.  (5.76 MB)
Linardos P, Mohedano E, Nieto JJosé, O'Connor N, Giró-i-Nieto X, McGuinness K. Simple vs complex temporal recurrences for video saliency prediction. In British Machine Vision Conference (BMVC). Cardiff, Wales / UK.: British Machine Vision Association; 2019.  (1.79 MB)
Linardos P, Mohedano E, Nieto JJosé, O'Connor N, Giró-i-Nieto X, McGuinness K. Simple vs complex temporal recurrences for video saliency prediction. In British Machine Vision Conference (BMVC). Cardiff, Wales / UK.: British Machine Vision Association; 2019.  (1.79 MB)
Kuijf H, Biesbroek M, de Bresser J, Heinen R, Andermatt S, Bento M, et al.. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. IEEE Transactions on Medical Imaging. 2019;.

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