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Perez-Cano J, Valero ISansano, Anglada D, Pina O, Salembier P, Marqués F. Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue. Heliyon. 2024;10(7).  (4.05 MB)
Pérez-Granero P. 2D to 3D body pose estimation for sign language with Deep Learning. McGuinness K, Giró-i-Nieto X. 2020.  (2.97 MB)
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B. Antipodally Invariant Metrics For Fast Regression-Based Super-Resolution. IEEE Transactions on Image Processing. 2016;25(6):2468.  (5.48 MB)
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B. Method for upscaling an image and apparatus for upscaling an image. US 20170132759 A1; 2018.
Perez-Pellitero E. Manifold Learning for Super Resolution. Rosenhahn B, Ruiz-Hidalgo J. [Hannover]: Leibniz Universität Hannover; 2017.  (18.6 MB)
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B. Bayesian region selection for adaptive dictionary-based Super-Resolution. In British Machine Vision Conference. 2013.  (2.59 MB)
Perez-Pellitero E, Salvador J, Torres-Xirau I, Ruiz-Hidalgo J, Rosenhahn B. Fast Super-Resolution via Dense Local Training and Inverse Regressor Search. In Asian Conference in Computer Vision (ACCV). Singapore; 2014.  (19.06 MB)
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B. PSyCo: Manifold Span Reduction for Super Resolution. In IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA; 2016.  (1.56 MB)
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B. Half Hypersphere Confinement for Piecewise Linear Regression. In IEEE Winter Conference on Applications of Computer Vision. Lake Placid, NY, USA; 2016.  (7.01 MB)
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B. Accelerating Super-Resolution for 4K Upscaling. In IEEE International Conference on Consumer Electronics. Las Vegas, NV, USA; 2015.  (1.07 MB)
Petras I, Beleznai C, Dedeoglu Y, Pardàs M, Kovács L, Szlávik Z, et al.. Flexible test-bed for unusual behavior detection. In 6th ACM International Conference on Image and Video Retrieval. 2007. pp. 105–108.
Petrone P, Casamitjana A, Falcon C, Artigues M, Operto G, Skouras S, et al.. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2018;14(7):P1235.
Petrone P, Vilaplana V, Casamitjana A, Tucholka A, Falcon C, Cacciaglia R, et al.. Magnetic Resonance Imaging as a valuable tool for Alzheimer's disease screening. In Alzheimer’s Association International Conference, London, 2017. 2017.
Petrone P, Vilaplana V, Casamitjana A, Sanchez-Escobedo D, Tucholka A, Cacciaglia R, et al.. Magnetic Resonance Imaging as a valuable tool for Alzheimer's disease screening. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2017;13(7):P1245.
Petrone P, Casamitjana A, Falcon C, Cànaves MArtigues, Operto G, Cacciaglia R, et al.. Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI. Alzheimer's Research & Therapy. 2019;11(1).
Petrone P, Casamitjana A, Falcon C, Artigues M, Operto G, Skouras S, et al.. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. In Alzheimer's Association International Conference. Chicago, USA; 2018.
Pina O, Dorca E, Vilaplana V. Cell-DETR: Efficient cell detection and classification in WSIs with transformers. In Medical Imaging with Deep Learning (MIDL 2024). 2024.
Pina O, Vilaplana V. Self-supervised graph representations of WSIs. In Geometric Deep Learning in Medical Image Analysis. 2022.
Pina O, Vilaplana V. Unsupervised Domain Adaptation for Multi-Stain Cell Detection in Breast Cancer with Transformers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (DEF-AI-MIA workshop). 2024.
Pina O, Vilaplana V. Feature propagation as self-supervision signals on graphs. Knowledge-Based Systems. 2024;289.
Pina O, Vilaplana V. Layer-wise self-supervised learning on graphs. In KDD 2023 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD 2023). Long Beach, USA; 2023.
Pina O, Cumplido-Mayoral I, Cacciaglia R, González-de-Echávarri JMaría, Gispert JD, Vilaplana V. Structural Networks for Brain Age Prediction. In Medical Imaging with Deep Learning (MIDL 2022). 2022.
Pinazo J, Lerín A, de Gibert FXavier, Moliner Á, Sevilla D, Jurado A, et al.. Perception in the era of Autonomous Vehicles. In Photonics 4 Smart Cities, SCEWC 2022. Barcelona: Photonics21; 2022.  (2.34 MB)
Pineda N, Jorge J, Garrido L, Salembier P. Estudio de campos de golf mediante técnicas de segmentación. In IX Congreso Nacional de Teledetección. Lleida, Spain; 2001.  (130.37 KB)
Plasencia AChávez, García-Gómez P, Pérez EBernal, de-Mas-Giménez G, Casas J, Royo S. A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection. Sensors. 2023;23(8).

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