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Author Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas
Title ICDAR2017 Robust Reading Challenge on COCO-Text Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ GSG2017 Serial (down) 3076
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Author Xavier Soria; Angel Sappa; Arash Akbarinia
Title Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities Type Conference Article
Year 2017 Publication 7th International Conference on Image Processing Theory, Tools & Applications Abbreviated Journal
Volume Issue Pages
Keywords Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset
Abstract Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images.
Address Montreal; Canada; 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 IPTA
Notes NEUROBIT; MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSA2017 Serial (down) 3074
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Learning structural loss parameters on graph embedding applied on symbolic graphs Type Conference Article
Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
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 GREC
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017b Serial (down) 3073
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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
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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 (down) 3069
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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 (down) 3068
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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 (down) 3067
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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 (down) 3066
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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 (down) 3065
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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 (down) 3063
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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
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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 (down) 3061
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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
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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 (down) 3060
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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 (down) 3059
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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 14th International Conference on Document Analysis and 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 ICDAR
Notes DAG; 600.097; 601.302; 600.121 Approved no
Call Number Admin @ si @ RDL2017b Serial (down) 3058
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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 14th International Conference on Document Analysis and 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 ICDAR
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ DDL2017 Serial (down) 3057
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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 (down) 3056
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