| Records |
| Author |
Dimosthenis Karatzas |
| Title |
Detecting Gradients in Text Images Using the Hough Transform |
Type |
Conference Article |
| Year |
2008 |
Publication  |
Proceedings of the 8th International Workshop on Document Analysis Systems, |
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| Volume |
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Pages |
245–252 |
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Nara (Japan) |
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DAG |
Approved |
no |
| Call Number |
DAG @ dag @ Kar2008 |
Serial |
1062 |
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| Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
| Title |
Writer Identification in Old Handwritten Music Scores |
Type |
Conference Article |
| Year |
2008 |
Publication |
Proceedings of the 8th International Workshop on Document Analysis Systems, |
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| Volume |
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Issue |
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Pages |
347–353 |
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Nara (Japan) |
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DAS |
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DAG |
Approved |
no |
| Call Number |
DAG @ dag @ FLS2008b |
Serial |
1078 |
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| Author |
Antonio Lopez; Cristina Cañero; Joan Serrat; J. Saludes; Felipe Lumbreras; T. Graf |
| Title |
Detection of lane markings based on ridgeness and RANSAC |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 733–738 |
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Pages |
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| Keywords |
lane markings |
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| Address |
Vienna (Austria) |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ LCS2005 |
Serial |
588 |
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| Author |
Daniel Ponsa; Antonio Lopez; Joan Serrat; Felipe Lumbreras; T. Graf |
| Title |
Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1108–1113, ISBN:0–7803–9216–7 |
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Issue |
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Pages |
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| Keywords |
vehicle detection |
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| Address |
Vienna (Austria) |
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ADAS |
Approved |
no |
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ADAS @ adas @ PLS2005 |
Serial |
615 |
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| Author |
Daniel Ponsa; Antonio Lopez; Felipe Lumbreras; Joan Serrat; T. Graf |
| Title |
3D Vehicle Sensor based on Monocular Vision |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1096–1101, ISBN:0–7803–9216–7 |
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Vienna (Austria) |
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ADAS |
Approved |
no |
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ADAS @ adas @ PLL2005 |
Serial |
614 |
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| Author |
Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey |
| Title |
Quality Improvement of Endoscopy Videos |
Type |
Conference Article |
| Year |
2011 |
Publication |
Proceedings of the 8th IASTED International Conference on Biomedical Engineering |
Abbreviated Journal |
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| Volume |
723 |
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800 |
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MV |
Approved |
no |
| Call Number |
fernando @ fernando @ |
Serial |
2426 |
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| Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
| Title |
Multi-oriented English Text Line Extraction using Background and Foreground Information |
Type |
Conference Article |
| Year |
2008 |
Publication |
Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
315–322 |
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| Address |
Nara (Japo) |
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DAS |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RPL2008b |
Serial |
1047 |
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| Author |
Marçal Rusiñol; Josep Llados |
| Title |
Word and Symbol Spotting using Spatial Organization of Local Descriptors |
Type |
Conference Article |
| Year |
2008 |
Publication |
Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
489–496 |
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| Address |
Nara (Japan) |
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DAS |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RuL2008b |
Serial |
1059 |
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| Author |
T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades |
| Title |
Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval |
Type |
Conference Article |
| Year |
2008 |
Publication |
Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
191-197 |
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| Address |
Nara, Japan |
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DAS |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ NTR2008a |
Serial |
1873 |
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| Author |
Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera |
| Title |
Video-based Skill Assessment for Golf: Estimating Golf Handicap |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
31-39 |
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| Abstract |
Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. |
| Address |
Otawa; Canada; October 2023 |
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Conference |
MMSports |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ KXC2023 |
Serial |
3929 |
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| Author |
Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes |
| Title |
ASTRA: An Action Spotting TRAnsformer for Soccer Videos |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
93–102 |
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| Abstract |
In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. |
| Address |
Otawa; Canada; October 2023 |
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MMSports |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ XEM2023 |
Serial |
3970 |
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| Author |
Francisco Javier Orozco; F.A. Garcia; J.L. Arcos; Jordi Gonzalez |
| Title |
Spatio-Temporal Reasoning for Reliable Facial Expression Interpretation |
Type |
Conference Article |
| Year |
2007 |
Publication |
Proceedings of the 5th International Conference on Computer Vision Systems |
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Bielefeld University (Germany) |
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ICVS |
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ISE |
Approved |
no |
| Call Number |
ISE @ ise @ OGA2007 |
Serial |
772 |
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| Author |
David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa |
| Title |
Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection |
Type |
Conference Article |
| Year |
2007 |
Publication |
Proceedings of the 5th International Conference on Computer Vision Systems |
Abbreviated Journal |
ICVS |
| Volume |
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| Keywords |
Pedestrian Detection |
| Abstract |
On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one. |
| Address |
Bielefeld (Germany) |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ gsl2007a |
Serial |
786 |
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| Author |
Maya Dimitrova; N. Kushmerick; Petia Radeva; Juan J. Villanueva |
| Title |
User Assesment of a Visual Genre Classifier |
Type |
Miscellaneous |
| Year |
2003 |
Publication |
Proceedings of the 3rd IASTED Int. Conference Visualization, Imaging and Image Processing |
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MILAB |
Approved |
no |
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BCNPCL @ bcnpcl @ DKR2003 |
Serial |
372 |
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| Author |
Mohamed Ali Souibgui; Sanket Biswas; Andres Mafla; Ali Furkan Biten; Alicia Fornes; Yousri Kessentini; Josep Llados; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 37th AAAI Conference on Artificial Intelligence |
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37 |
Issue |
2 |
Pages |
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| Keywords |
Representation Learning for Vision; CV Applications; CV Language and Vision; ML Unsupervised; Self-Supervised Learning |
| Abstract |
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR |
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AAAI |
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DAG |
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no |
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Admin @ si @ SBM2023 |
Serial |
3848 |
| Permanent link to this record |
