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Author Nuria Cirera edit  openurl
  Title (up) Recognition of Handwritten Historical Documents Type Report
  Year 2012 Publication CVC Technical Report Abbreviated Journal  
  Volume 174 Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis Master's thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
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  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 Ernest Valveny; Enric Marti edit  openurl
  Title (up) 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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  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ VAM1999 Serial 1658  
Permanent link to this record
 

 
Author Partha Pratim Roy; Umapada Pal; Josep Llados edit  openurl
  Title (up) 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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  Address  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICVGIP ’08  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RPL2008c Serial 1080  
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Author Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil edit  doi
openurl 
  Title (up) 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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM; ADAS; 600.139; 600.145; 600.118 Approved no  
  Call Number Admin @ si @ HYF2022 Serial 3720  
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Author Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez edit  doi
openurl 
  Title (up) 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.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  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  
Permanent link to this record
 

 
Author Fadi Dornaika; Bogdan Raducanu edit  openurl
  Title (up) 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  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Sydney (Australia)  
  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 OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ DoR2006 Serial 799  
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Author Md.Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig edit  url
doi  openurl
  Title (up) 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 Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez edit  url
doi  openurl
  Title (up) 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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  Notes LAMP; ADAS; 600.118; 600.120 Approved no  
  Call Number Admin @ si @ VWL2020 Serial 3405  
Permanent link to this record
 

 
Author M. Navarro edit  openurl
  Title (up) Reconeixement d´objectes amb metodes basats en color: avaluacio en un entorn poc controlat Type Report
  Year 1999 Publication CVC Technical Report #28 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address CVC (UAB)  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Nav1999 Serial 190  
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Author David Guillamet edit  openurl
  Title (up) Reconeixement d´objectes en entorns poc controlats mitjançant metodes estadistics Type Report
  Year 1999 Publication CVC Technical Report #30 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address CVC (UAB)  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Gui1999 Serial 51  
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Author V. Chapaprieta edit  openurl
  Title (up) Reconocimiento de caracteres manuscritos mediante modelos de distribucion de puntos (PDM) Type Report
  Year 2000 Publication CVC Technical Report #41 Abbreviated Journal  
  Volume Issue Pages  
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  Address CVC (UAB)  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Cha2000 Serial 342  
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Author Joan Carbo; A. Martinez; Jordi Vitria edit  openurl
  Title (up) Reconocimiento de caras Type Report
  Year 1996 Publication CVC Technical Report #18 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address CVC (UAB)  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ CMV1996 Serial 529  
Permanent link to this record
 

 
Author Enric Marti; Jordi Vitria; Alberto Sanfeliu edit   pdf
isbn  openurl
  Title (up) Reconocimiento de Formas y Análisis de Imágenes Type Book Whole
  Year 1998 Publication Asociación Española de Reconocimientos de Formas y Análisis de Imágenes Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Los sistemas actuales de reconocimiento automático del lenguaje oral se basan en dos etapas básicas de procesado: la parametrización, que extrae la evolución temporal de los parámetros que caracterizan la voz, y el reconocimiento propiamente dicho, que identifica la cadena de palabras de la elocución recibida con ayuda de los modelos que representan el conocimiento adquirido en la etapa de aprendizaje. Tomando como línea divisoria la palabra, dichos modelos son de tipo acústicofonético o gramatical. Los primeros caracterizan las palabras incluidas en el vocabulario de la aplicación o tarea a la que está orientado el sistema de reconocimiento, usando a menudo para ello modelos de unidades de habla de extensión inferior a la palabra, es decir, de unidades subléxicas. Por otro lado, la gramática incluye el conocimiento acerca de las combinaciones permitidas de palabras para formar las frases o su probabilidad. Queda fuera del esquema la denominada comprensión del habla, que utiliza adicionalmente el conocimiento semántico y pragmático para captar el significado de la elocución de entrada al sistema a partir de la cadena (o cadenas alternativas) de palabras que suministra el reconocedor.  
  Address  
  Corporate Author Thesis  
  Publisher AERFAI Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 84–922529–4–4 Medium  
  Area Expedition Conference  
  Notes IAM;OR;MV Approved no  
  Call Number IAM @ iam @ MVS1998 Serial 1620  
Permanent link to this record
 

 
Author M.Gomez; Josefina Mauri; Eduard Fernandez-Nofrerias; Oriol Rodriguez-Leon; Carme Julia; Debora Gil; Petia Radeva edit  openurl
  Title (up) Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria Type Conference Article
  Year 2002 Publication XXXVIII Congreso Nacional de la Sociedad Española de Cardiología. Abbreviated Journal  
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  Area Expedition Conference  
  Notes IAM;ADAS;MILAB Approved no  
  Call Number IAM @ iam @ GMF2002d Serial 1516  
Permanent link to this record
 

 
Author X. Binefa; Jordi Vitria; Maria Vanrell edit  openurl
  Title (up) Reconstruccion tridimensional de imagenes Microscopicas. Type Miscellaneous
  Year 1992 Publication V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes Abbreviated Journal  
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  Area Expedition Conference  
  Notes OR;CIC;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BVV1992b Serial 255  
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