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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
	Keywords
	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
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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
	Keywords
	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.
	Address
	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	1083-4419	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; DAG;HuPBA			Approved	no
	Call Number	Admin @ si @ EFP2011			Serial	1784
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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
	Keywords
	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
	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	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; 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
	Keywords
	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.
	Address
	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	HuPBA; DAG; MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ EFP2009a			Serial	1180
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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
	Keywords
	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.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication	Berlin	Editor	H. Kawasnicka; L.Jain
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	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
	Title	Fast traffic model matching and recognition on gray-scale images			Type	Report
	Year	2005	Publication	CVC Technical Report #84	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	CVC (UAB)
	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	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
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	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	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
	Keywords
	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.
	Address
	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	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
	Keywords
	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
	Corporate Author				Thesis
	Publisher	Springer Heidelberg	Place of Publication		Editor	F.J. Perales; R.B. Fisher; T.B. Moeslund
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	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
	Volume		Issue		Pages
	Keywords
	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.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Petia Radeva;Oriol Pujol
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ Esc2008b			Serial	2217
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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.
	Address	Barcelona; June 2017
	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	COSECIVI; CEUR-WS
	Notes	HUPBA; no menciona;MILAB			Approved	no
	Call Number	Admin @ si @ AED2017			Serial	3065
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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
	Keywords
	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
	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	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	Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol
	Title	Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning			Type	Conference Article
	Year	2023	Publication	17th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	14192	Issue		Pages	106-121
	Keywords	Scene Text Detection; Scene Text Recognition; Transformer Acceleration
	Abstract	Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds.
	Address	San Jose; CA; USA; August 2023
	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	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GKR2023a			Serial	3907
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	Author	Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol
	Title	STEP – Towards Structured Scene-Text Spotting			Type	Conference Article
	Year	2024	Publication	Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	883-892
	Keywords
	Abstract	We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios.
	Address	Waikoloa; Hawai; USA; January 2024
	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	WACV
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GKR2024			Serial	3992
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	Author	Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas
	Title	Out-of-Vocabulary Challenge Report			Type	Conference Article
	Year	2022	Publication	Proceedings European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume	13804	Issue		Pages	359–375
	Keywords
	Abstract	This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.
	Address	Tel-Aviv; Israel; October 2022
	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	ECCVW
	Notes	DAG; 600.155; 302.105; 611.002			Approved	no
	Call Number	Admin @ si @ GMB2022			Serial	3771
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