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Author  |
Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas | ||||
| Title | Read While You Drive-Multilingual Text Tracking on the Road | Type | Conference Article | ||
| Year | 2022 | Publication | 15th IAPR International workshop on document analysis systems | Abbreviated Journal | |
| Volume | 13237 | Issue | Pages | 756–770 | |
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| Abstract | Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild. | ||||
| Address | La Rochelle; France; May 2022 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | 978-3-031-06554-5 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.155; 611.022; 611.004 | Approved | no | ||
| Call Number | Admin @ si @ GTR2022 | Serial | 3783 | ||
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| Author |
Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera | ||||
| Title | XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system | Type | Conference Article | ||
| Year | 2017 | Publication | 4th Congreso de la Sociedad Española para las Ciencias del Videojuego | Abbreviated Journal | |
| Volume | 1957 | Issue | Pages | 13-28 | |
| Keywords | Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris | ||||
| Abstract | XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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| Address | Barcelona; June 2017 | ||||
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| Area | Expedition | Conference | COSECIVI; CEUR-WS | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ AED2017 | Serial | 3065 | ||
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| Author |
Sergio Escalera | ||||
| Title | Fast traffic model matching and recognition on gray-scale images | Type | Report | ||
| Year | 2005 | Publication | CVC Technical Report #84 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2005 | Serial | 572 | ||
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| Author |
Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-Class Pattern and Object Recognition | Type | Miscellaneous | ||
| Year | 2008 | Publication | Abbreviated Journal | ||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2008a | Serial | 1106 | ||
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| Author |
Sergio Escalera | ||||
| Title | Multi-Modal Human Behaviour Analysis from Visual Data Sources | Type | Journal | ||
| Year | 2013 | Publication | ERCIM News journal | Abbreviated Journal | ERCIM |
| Volume | 95 | Issue | Pages | 21-22 | |
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| Abstract | The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction. | ||||
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| ISSN | 0926-4981 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ Esc2013 | Serial | 2361 | ||
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| Author |
Sergio Escalera | ||||
| Title | Human Behavior Analysis From Depth Maps | Type | Conference Article | ||
| Year | 2012 | Publication | 7th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 7378 | Issue | Pages | 282-292 | |
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| Abstract | Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. | ||||
| Address | Mallorca | ||||
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| Publisher | Springer Heidelberg | Place of Publication | Editor | F.J. Perales; R.B. Fisher; T.B. Moeslund | |
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| ISSN | 0302-9743 | ISBN | 978-3-642-31566-4 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2012 | Serial | 2040 | ||
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| Author |
Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A | Type | Book Whole | ||
| Year | 2008 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However, the extension of many 2-class classifiers to the multi-class case is a hard task. In this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful tool to combine any number of binary classifiers to model multi-class problems. But there are still many open issues about the capabilities of the ECOC framework. In this thesis, the two main stages of an ECOC design are analyzed: the coding and the decoding steps. We present different problem-dependent designs. These designs take advantage of the knowledge of the problem domain to minimize the number of classifiers, obtaining a high classification performance. On the other hand, we analyze the ECOC codification in order to define new decoding rules that take full benefit from the information provided at the coding step. Moreover, as a successful classification requires a rich feature set, new feature detection/extraction techniques are presented and evaluated on the new ECOC designs. The evaluation of the new methodology is performed on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease data set. The results of this thesis show that significant performance improvements are obtained on both traditional coding and decoding ECOC designs when the new coding and decoding rules are taken into account. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2008b | Serial | 2217 | ||
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| Author |
Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin | ||||
| Title | Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes | Type | Book Chapter | ||
| Year | 2011 | Publication | Innovations in Intelligent Image Analysis | Abbreviated Journal | |
| Volume | 339 | Issue | Pages | 7-29 | |
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| Abstract | A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Berlin | Editor | H. Kawasnicka; L.Jain |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1860-949X | ISBN | 978-3-642-17933-4 | Medium | |
| Area | Expedition | Conference | |||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ETP2011 | Serial | 1746 | ||
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| Author |
Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados | ||||
| Title | Blurred Shape Model for Binary and Grey-level Symbol Recognition | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 30 | Issue | 15 | Pages | 1424–1433 |
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| Abstract | Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. | ||||
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| Notes | HuPBA; DAG; MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EFP2009a | Serial | 1180 | ||
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| Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva | ||||
| Title | Circular Blurred Shape Model for Symbol Spotting in Documents | Type | Conference Article | ||
| Year | 2009 | Publication | 16th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 1985-1988 | ||
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| Abstract | Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. | ||||
| Address | Cairo, Egypt | ||||
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| ISSN | ISBN | 978-1-4244-5653-6 | Medium | ||
| Area | Expedition | Conference | ICIP | ||
| Notes | MILAB;HuPBA;DAG | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EFP2009b | Serial | 1184 | ||
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| Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva | ||||
| Title | Multi-class Binary Object Categorization using Blurred Shape Models | Type | Conference Article | ||
| Year | 2007 | Publication | 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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| Series Editor | Series Title | Abbreviated Series Title | LCNS | ||
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| 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 | ||
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| Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva | ||||
| Title | Circular Blurred Shape Model for Multiclass Symbol Recognition | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) | Abbreviated Journal | TSMCB |
| Volume | 41 | Issue | 2 | Pages | 497-506 |
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| Abstract | In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. | ||||
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| ISSN | 1083-4419 | ISBN | Medium | ||
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| Notes | MILAB; DAG;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EFP2011 | Serial | 1784 | ||
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| Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva | ||||
| Title | Multi-class Binary Symbol Classification with Circular Blurred Shape Models | Type | Conference Article | ||
| Year | 2009 | Publication | 15th International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 5716 | Issue | Pages | 1005–1014 | |
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| Abstract | Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. | ||||
| Address | Salerno, Italy | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-04145-7 | Medium | |
| Area | Expedition | Conference | ICIAP | ||
| Notes | MILAB;HuPBA;DAG | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EFP2009c | Serial | 1186 | ||
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| Author |
Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo | ||||
| Title | Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2011 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
| Volume | 30 | Issue | 7 | Pages | 2107-2115 |
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| Abstract | IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EPA2011 | Serial | 1881 | ||
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| Author |
Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin | ||||
| Title | Subclass Problem-Dependent Design for Error-Correcting Output Codes | Type | Journal | ||
| Year | 2008 | Publication | IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054 | Abbreviated Journal | |
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ ETP2008 | Serial | 951 | ||
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