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Author Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez
Title (down) Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
Year 2020 Publication Sensors Abbreviated Journal SENS
Volume 20 Issue 3 Pages 583
Keywords
Abstract On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.
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Area Expedition Conference
Notes LAMP; ADAS; 600.118; 600.120 Approved no
Call Number Admin @ si @ VWL2020 Serial 3405
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Author Md.Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig
Title (down) Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism Type Journal Article
Year 2019 Publication IEEE Access Abbreviated Journal ACCESS
Volume 7 Issue Pages 39069-39082
Keywords
Abstract Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called “EgoFoodPlaces” that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the “EgoFoodPlaces” dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3.
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Notes MILAB; no menciona Approved no
Call Number Admin @ si @ SRA2019 Serial 3296
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Author Fadi Dornaika; Bogdan Raducanu
Title (down) Recognizing Facial Expressions in Videos Using a Facial Action Analysis-Synthesis Scheme Type Miscellaneous
Year 2006 Publication International Conference on Advanced Video and Signal Based Surveillance, (AVSS 2006), ISBN: 0–7695–2688–8 Abbreviated Journal
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Address Sydney (Australia)
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ DoR2006 Serial 799
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Author Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez
Title (down) Recognizing Actions through Action-specific Person Detection Type Journal Article
Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 24 Issue 11 Pages 4422-4432
Keywords
Abstract Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test tim- , outperforms on both data sets state-of-the-art methods, which do use person locations.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1057-7149 ISBN Medium
Area Expedition Conference
Notes ADAS; LAMP; 600.076; 600.079 Approved no
Call Number Admin @ si @ KXR2015 Serial 2668
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Author Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil
Title (down) Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals Type Journal Article
Year 2022 Publication Applied Sciences Abbreviated Journal APPLSCI
Volume 12 Issue 5 Pages 2298
Keywords Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion
Abstract The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.
Address February 2022
Corporate Author Thesis
Publisher Place of Publication Editor
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Notes IAM; ADAS; 600.139; 600.145; 600.118 Approved no
Call Number Admin @ si @ HYF2022 Serial 3720
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Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title (down) Recognition of Multi-oriented Touching Characters in Graphical Documents Type Conference Article
Year 2008 Publication Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, Abbreviated Journal
Volume 16 Issue Pages 297–304
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Area Expedition Conference ICVGIP ’08
Notes DAG Approved no
Call Number DAG @ dag @ RPL2008c Serial 1080
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Author Ernest Valveny; Enric Marti
Title (down) Recognition of lineal symbols in hand-written drawings using deformable template matching Type Conference Article
Year 1999 Publication 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 Nuria Cirera
Title (down) Recognition of Handwritten Historical Documents Type Report
Year 2012 Publication CVC Technical Report Abbreviated Journal
Volume 174 Issue Pages
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Abstract
Address
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ Cir2012 Serial 2416
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title (down) Recoding Error-Correcting Output Codes Type Conference Article
Year 2009 Publication 8th International Workshop of Multiple Classifier Systems Abbreviated Journal
Volume 5519 Issue Pages 11–21
Keywords
Abstract One of the most widely applied techniques to deal with multi- class categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and classified according to a comparison with the positions of the coded matrix. In this paper, we present a novel approach to redefine without retraining, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information increases the generalization capability of the system. Moreover, the final classification can be tuned with the inclusion of a weighting matrix in the decoding step. The approach has been validated over several UCI Machine Learning repository data sets and two real multi-class problems: traffic sign and face categorization. The results show that performance improvements are obtained when comparing the new approach to one of the best ECOC designs (one-versus-one). Furthermore, the novel methodology obtains at least the same performance than the one-versus-one ECOC design.
Address Reykjavik (Iceland)
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-02325-5 Medium
Area Expedition Conference MCS
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2009d Serial 1190
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Author Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados
Title (down) Recherche de sous-graphes par encapsulation floue des cliques d'ordre 2: Application à la localisation de contenu dans les images de documents graphiques Type Conference Article
Year 2012 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal
Volume Issue Pages 149-162
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Abstract
Address
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CIFED
Notes DAG Approved no
Call Number Admin @ si @ LBR2012 Serial 2382
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Author Jordi Vitria; Petia Radeva; I. Aguilo
Title (down) Recent Advances in Artificial Intelligence Research and Development Type Book Chapter
Year 2004 Publication Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9 Abbreviated Journal
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Address Amsterdam
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VRA2004 Serial 509
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Author Fadi Dornaika; Angel Sappa
Title (down) Real-time Vehicle Ego-Motion using Stereo Pairs and Particle Filters Type Conference Article
Year 2007 Publication Int. Conf. on Image Analysis and Recognition, Abbreviated Journal
Volume 4633 Issue Pages 469–480
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Abstract
Address Montreal (Canada)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ DoS2007a Serial 813
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Author Antonio Lopez; Ernest Valveny; Juan J. Villanueva
Title (down) Real-time quality control of surgical material packaging by artificial vision Type Journal Article
Year 2005 Publication Assembly Automation Abbreviated Journal
Volume 25 Issue 3 Pages
Keywords
Abstract IF: 0.061)
Address
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 ADAS;DAG Approved no
Call Number ADAS @ adas @ LVV2005 Serial 552
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Author Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace
Title (down) Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis Type Conference Article
Year 2017 Publication 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
Volume Issue Pages
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Address Barcelona; Spain; June 2017
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 CARS
Notes MV; no menciona Approved no
Call Number Admin @ si @ ABS2017 Serial 2947
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Author E. Bondi ; L. Sidenari; Andrew Bagdanov; Alberto del Bimbo
Title (down) Real-time people counting from depth imagery of crowded environments Type Conference Article
Year 2014 Publication 11th IEEE International Conference on Advanced Video and Signal based Surveillance Abbreviated Journal
Volume Issue Pages 337 - 342
Keywords
Abstract In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications.
Address Seoul; Korea; August 2014
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 AVSS
Notes LAMP; 600.079 Approved no
Call Number Admin @ si @ BSB2014 Serial 2540
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