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	Author	Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados
	Title	Handwriting Recognition by Attribute embedding and Recurrent Neural Networks			Type	Conference Article
	Year	2017	Publication	14th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1038-1043
	Keywords
	Abstract	Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently, these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model
	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	ICDAR
	Notes	DAG; 600.097; 601.225; 600.121			Approved	no
	Call Number	Admin @ si @ TDF2017			Serial	3055
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	Author	Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
	Title	Optical Music Recognition by Recurrent Neural Networks			Type	Conference Article
	Year	2017	Publication	14th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	25-26
	Keywords	Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory
	Abstract	Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level
	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	ICDAR
	Notes	DAG; 600.097; 601.302; 600.121			Approved	no
	Call Number	Admin @ si @ BRC2017			Serial	3056
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	Author	Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal
	Title	Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario			Type	Conference Article
	Year	2017	Publication	12th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	31-32
	Keywords
	Abstract	One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
	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	GREC
	Notes	DAG; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ DDL2017			Serial	3057
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	Author	Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes
	Title	Graph-based deep learning for graphics classification			Type	Conference Article
	Year	2017	Publication	12th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	29-30
	Keywords
	Abstract	Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and we show how they can be used in graphics recognition problems
	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	GREC
	Notes	DAG; 600.097; 601.302; 600.121			Approved	no
	Call Number	Admin @ si @ RDL2017b			Serial	3058
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	Author	Adria Rico; Alicia Fornes
	Title	Camera-based Optical Music Recognition using a Convolutional Neural Network			Type	Conference Article
	Year	2017	Publication	12th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	27-28
	Keywords	optical music recognition; document analysis; convolutional neural network; deep learning
	Abstract	Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results
	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	GREC
	Notes	DAG;600.097; 600.121			Approved	no
	Call Number	Admin @ si @ RiF2017			Serial	3059
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	Author	Oriol Vicente; Alicia Fornes; Ramon Valdes
	Title	La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades			Type	Conference Article
	Year	2017	Publication	3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional	Abbreviated Journal
	Volume		Issue		Pages	281-383
	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	978-84-697-5692-8	Medium
	Area		Expedition		Conference	HDH
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ VFV2017			Serial	3060
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	Author	Alicia Fornes; Beata Megyesi; Joan Mas
	Title	Transcription of Encoded Manuscripts with Image Processing Techniques			Type	Conference Article
	Year	2017	Publication	Digital Humanities Conference	Abbreviated Journal
	Volume		Issue		Pages	441-443
	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	DH
	Notes	DAG; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ FMM2017			Serial	3061
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	Author	Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri
	Title	Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction			Type	Journal Article
	Year	2018	Publication	Journal of Mathematical Imaging and Vision	Abbreviated Journal	JMIV
	Volume	60	Issue	4	Pages	512-524
	Keywords
	Abstract	This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
	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	DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129			Approved	no
	Call Number	Admin @ si @ DMH2018a			Serial	3062
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	Author	Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol
	Title	The Robust Reading Competition Annotation and Evaluation Platform			Type	Conference Article
	Year	2017	Publication	1st International Workshop on Open Services and Tools for Document Analysis	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services
	Address	Kyoto; Japan; November 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	ICDAR-OST
	Notes	DAG; 600.084; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ KGR2017			Serial	3063
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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			Approved	no
	Call Number	Admin @ si @ AED2017			Serial	3065
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	Author	Jun Wan; Sergio Escalera; Gholamreza Anbarjafari; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Meysam Madadi; Juri Allik; Jelena Gorbova; Chi Lin; Yiliang Xie
	Title	Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges			Type	Conference Article
	Year	2017	Publication	Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field.
	Address	Venice; Italy; October 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	ICCVW
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ WEA2017			Serial	3066
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	Author	Yagmur Gucluturk; Umut Guclu; Marc Perez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Carlos Andujar; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera
	Title	Visualizing Apparent Personality Analysis with Deep Residual Networks			Type	Conference Article
	Year	2017	Publication	Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV	Abbreviated Journal
	Volume		Issue		Pages	3101-3109
	Keywords
	Abstract	Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called “looking at people” sub-field. Considering “apparent” personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining model predictions with their explanations.
	Address	Venice; Italy; October 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	ICCVW
	Notes	HUPBA; 6002.143			Approved	no
	Call Number	Admin @ si @ GGP2017			Serial	3067
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	Author	Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera
	Title	Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies			Type	Conference Article
	Year	2017	Publication	Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Venice; Italy; October 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	ICCVW
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ ABR2017			Serial	3068
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	Author	Albert Clapes; Tinne Tuytelaars; Sergio Escalera
	Title	Darwintrees for action recognition			Type	Conference Article
	Year	2017	Publication	Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV	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	ICCVW
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ CTE2017			Serial	3069
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	Author	Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund
	Title	Back-dropout Transfer Learning for Action Recognition			Type	Journal Article
	Year	2018	Publication	IET Computer Vision	Abbreviated Journal	IETCV
	Volume	12	Issue	4	Pages	484-491
	Keywords	Learning (artificial intelligence); Pattern Recognition
	Abstract	Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.
	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; no proj			Approved	no
	Call Number	Admin @ si @ RKM2018			Serial	3071
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