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Author Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras edit  url
openurl 
  Title Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification Type Conference Article
  Year 2023 Publication (down) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications Abbreviated Journal  
  Volume 14469 Issue Pages 214–228  
  Keywords  
  Abstract Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CIARP  
  Notes MSIAU Approved no  
  Call Number Admin @ si @ IBP2023 Serial 4008  
Permanent link to this record
 

 
Author Oriol Pujol; Petia Radeva; Jordi Vitria; J. Mauri edit  openurl
  Title Adaboost to Classify Plaque Appearance in IVUS Images Type Miscellaneous
  Year 2004 Publication (down) Progress in Pattern Recognition, Image Analysis and Applications, LNCS 3287:629–636 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Puebla (Mexico)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;MILAB;HuPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ PRV2004 Serial 472  
Permanent link to this record
 

 
Author Santiago Segui; Laura Igual; Petia Radeva; C. Malagelada; Fernando Azpiroz; Jordi Vitria edit  openurl
  Title A Semi-Supervised Learning Method for Motility Disease Diagnostic Type Book Chapter
  Year 2007 Publication (down) Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern (CIARP 2007), LCNS 4756:773–782, ISBN 978–3–540–76724–4 Abbreviated Journal  
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  Area Expedition Conference  
  Notes OR;MILAB;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ SIR2007b Serial 897  
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Author Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva edit  isbn
openurl 
  Title Multi-class Binary Object Categorization using Blurred Shape Models Type Conference Article
  Year 2007 Publication (down) Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern Abbreviated Journal  
  Volume 4756 Issue Pages 773–782  
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  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LCNS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-540-76724-4 Medium  
  Area Expedition Conference CIARP  
  Notes MILAB; DAG;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EFP2007 Serial 911  
Permanent link to this record
 

 
Author F. Javier Sanchez; Jordi Vitria edit  isbn
openurl 
  Title ViLi + : Extended Lisp for image Processing and Computer Vision. Type Conference Article
  Year 1994 Publication (down) Progress in Image Analysis and Processing III Abbreviated Journal  
  Volume Issue Pages  
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  Corporate Author Thesis  
  Publisher World Scientific Place of Publication Editor S.Impedovo  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 981-02-1552-5 Medium  
  Area Expedition Conference  
  Notes MV;OR Approved no  
  Call Number BCNPCL @ bcnpcl @ SaV1994; IAM @ iam @ SaV1994 Serial 114  
Permanent link to this record
 

 
Author Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis edit  openurl
  Title Prior Knowledge Based Motion Model Representation Type Book Chapter
  Year 2009 Publication (down) Progress in Computer Vision and Image Analysis Abbreviated Journal  
  Volume 16 Issue Pages  
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  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Horst Bunke; JuanJose Villanueva; Gemma Sanchez  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SAM2009 Serial 1235  
Permanent link to this record
 

 
Author Victor Campmany; Sergio Silva; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title GPU-based pedestrian detection for autonomous driving Type Abstract
  Year 2015 Publication (down) Programming and Tunning Massive Parallel Systems Abbreviated Journal PUMPS  
  Volume Issue Pages  
  Keywords Autonomous Driving; ADAS; CUDA; Pedestrian Detection  
  Abstract Pedestrian detection for autonomous driving has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. The real-time constraints in the field are tight, and regular processors are not able to handle the workload obtaining an acceptable ratio of frames per second (fps). Moreover, multiple cameras are required to obtain accurate results, so the need to speed up the process is even higher. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system. Further, we introduce significant algorithmic adjustments and optimizations to adapt the problem to the GPU architecture. The aim is to provide a system capable of running in real-time obtaining reliable results.  
  Address Barcelona; Spain  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title PUMPS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference PUMPS  
  Notes ADAS; 600.076; 600.082; 600.085 Approved no  
  Call Number ADAS @ adas @ CSM2015 Serial 2644  
Permanent link to this record
 

 
Author Sergio Silva; Victor Campmany; Laura Sellart; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title Autonomous GPU-based Driving Type Abstract
  Year 2015 Publication (down) Programming and Tunning Massive Parallel Systems Abbreviated Journal PUMPS  
  Volume Issue Pages  
  Keywords Autonomous Driving; ADAS; CUDA  
  Abstract Human factors cause most driving accidents; this is why nowadays is common to hear about autonomous driving as an alternative. Autonomous driving will not only increase safety, but also will develop a system of cooperative self-driving cars that will reduce pollution and congestion. Furthermore, it will provide more freedom to handicapped people, elderly or kids.

Autonomous Driving requires perceiving and understanding the vehicle environment (e.g., road, traffic signs, pedestrians, vehicles) using sensors (e.g., cameras, lidars, sonars, and radars), selflocalization (requiring GPS, inertial sensors and visual localization in precise maps), controlling the vehicle and planning the routes. These algorithms require high computation capability, and thanks to NVIDIA GPU acceleration this starts to become feasible.

NVIDIA® is developing a new platform for boosting the Autonomous Driving capabilities that is able of managing the vehicle via CAN-Bus: the Drive™ PX. It has 8 ARM cores with dual accelerated Tegra® X1 chips. It has 12 synchronized camera inputs for 360º vehicle perception, 4G and Wi-Fi capabilities allowing vehicle communications and GPS and inertial sensors inputs for self-localization.

Our research group has been selected for testing Drive™ PX. Accordingly, we are developing a Drive™ PX based autonomous car. Currently, we are porting our previous CPU based algorithms (e.g., Lane Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection, or Semantic Segmentation) for running in the GPU.
 
  Address Barcelona; Spain  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference PUMPS  
  Notes ADAS; 600.076; 600.082; 600.085 Approved no  
  Call Number ADAS @ adas @ SCS2015 Serial 2645  
Permanent link to this record
 

 
Author Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez edit   pdf
openurl 
  Title Stereo Matching using SGM on the GPU Type Report
  Year 2016 Publication (down) Programming and Tuning Massively Parallel Systems Abbreviated Journal PUMPS  
  Volume Issue Pages  
  Keywords CUDA; Stereo; Autonomous Vehicle  
  Abstract Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.  
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  Area Expedition Conference PUMPS  
  Notes ADAS; 600.085; 600.087; 600.076 Approved no  
  Call Number ADAS @ adas @ HCE2016b Serial 2776  
Permanent link to this record
 

 
Author Oriol Pujol; Misael Rosales; Petia Radeva; E Fernandez-Nofrerias edit  openurl
  Title Intravascular Ultrasound Images Vessel Characterization using AdaBoost Type Miscellaneous
  Year 2003 Publication (down) Proceedings on FIMH Abbreviated Journal  
  Volume Issue Pages  
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  Address Lyon, France  
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  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PRR2003a Serial 400  
Permanent link to this record
 

 
Author Dimosthenis Karatzas;Ch. Lioutas edit  openurl
  Title Software Package Development for Electron Diffraction Image Analysis Type Conference Article
  Year 1998 Publication (down) Proceedings of the XIV Solid State Physics National Conference Abbreviated Journal  
  Volume Issue Pages  
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  Address Ioannina, Greece  
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  Notes DAG Approved no  
  Call Number IAM @ iam @ KaL1998 Serial 2045  
Permanent link to this record
 

 
Author Mohammed Al Rawi; Dimosthenis Karatzas edit   pdf
openurl 
  Title On the Labeling Correctness in Computer Vision Datasets Type Conference Article
  Year 2018 Publication (down) Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a
problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect
labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble.
 
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  Area Expedition Conference ECML-PKDDW  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ RaK2018 Serial 3144  
Permanent link to this record
 

 
Author Ernest Valveny; Enric Marti edit  openurl
  Title Recognition of lineal symbols in hand-written drawings using deformable template matching Type Conference Article
  Year 1999 Publication (down) Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes Abbreviated Journal  
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  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ VAM1999 Serial 1658  
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Author Antonio Lopez; Ricardo Toledo; Joan Serrat; Juan J. Villanueva edit  openurl
  Title Extraction of vessel centerlines from 2D coronary angiographies Type Miscellaneous
  Year 1999 Publication (down) Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. pgs. 489–496, volume I Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Bilbao  
  Corporate Author Thesis  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ LTS1999 Serial 14  
Permanent link to this record
 

 
Author A. Pujol; Felipe Lumbreras; X. Varona; Juan J. Villanueva edit  openurl
  Title Template matching through invariant eigenspace projection. Type Miscellaneous
  Year 1999 Publication (down) Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Bilbao  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ PLV1999 Serial 6  
Permanent link to this record
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