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Author Hans Stadthagen-Gonzalez; Luis Lopez; M. Carmen Parafita; C. Alejandro Parraga
Title Using two-alternative forced choice tasks and Thurstone law of comparative judgments for code-switching research Type Book Chapter
Year 2018 Publication Linguistic Approaches to Bilingualism Abbreviated Journal
Volume Issue Pages 67-97
Keywords two-alternative forced choice and Thurstone's law; acceptability judgment; code-switching
Abstract This article argues that 2-alternative forced choice tasks and Thurstone’s law of comparative judgments (Thurstone, 1927) are well suited to investigate code-switching competence by means of acceptability judgments. We compare this method with commonly used Likert scale judgments and find that the 2-alternative forced choice task provides granular details that remain invisible in a Likert scale experiment. In order to compare and contrast both methods, we examined the syntactic phenomenon usually referred to as the Adjacency Condition (AC) (apud Stowell, 1981), which imposes a condition of adjacency between verb and object. Our interest in the AC comes from the fact that it is a subtle feature of English grammar which is absent in Spanish, and this provides an excellent springboard to create minimal code-switched pairs that allow us to formulate a clear research question that can be tested using both methods.
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Notes NEUROBIT; no menciona Approved no
Call Number Admin @ si @ SLP2018 Serial (down) 2994
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Author Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera
Title Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey Type Book Chapter
Year 2017 Publication Gesture Recognition Abbreviated Journal
Volume Issue Pages 539-578
Keywords Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies
Abstract Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ ACB2017a Serial (down) 2981
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Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
Title Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary Type Book Chapter
Year 2016 Publication Recent Trends in Image Processing and Pattern Recognition Abbreviated Journal
Volume 709 Issue Pages
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Area Expedition Conference RTIP2R
Notes DAG Approved no
Call Number Admin @ si @ HTR2016 Serial (down) 2956
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Author Pau Riba; Alicia Fornes; Josep Llados
Title Towards the Alignment of Handwritten Music Scores Type Book Chapter
Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages 103-116
Keywords Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment
Abstract It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
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Publisher Place of Publication Editor Bart Lamiroy; R Dueire Lins
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-52158-9 Medium
Area Expedition Conference
Notes DAG; 600.097; 602.006; 600.121 Approved no
Call Number Admin @ si @ RFL2017 Serial (down) 2955
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Author H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil
Title Medial structure generation for registration of anatomical structures Type Book Chapter
Year 2017 Publication Skeletonization, Theory, Methods and Applications Abbreviated Journal
Volume 11 Issue Pages
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Notes IAM; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ MFV2017a Serial (down) 2935
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Author Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel
Title Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition Type Book Chapter
Year 2017 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages
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Publisher Springer Place of Publication Editor B. Lamiroy; R Dueire Lins
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JLR2017 Serial (down) 2928
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre
Title Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources Type Book Chapter
Year 2016 Publication The future of historical demography. Upside down and inside out Abbreviated Journal
Volume Issue Pages 127-131
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Publisher Acco Publishers Place of Publication Editor K.Matthijs; S.Hin; H.Matsuo; J.Kok
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-94-6292-722-3 Medium
Area Expedition Conference
Notes DAG; 600.097 Approved no
Call Number Admin @ si @ PFL2016 Serial (down) 2907
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Author Marçal Rusiñol; Josep Llados
Title Flowchart Recognition in Patent Information Retrieval Type Book Chapter
Year 2017 Publication Current Challenges in Patent Information Retrieval Abbreviated Journal
Volume 37 Issue Pages 351-368
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Publisher Springer Berlin Heidelberg Place of Publication Editor M. Lupu; K. Mayer; N. Kando; A.J. Trippe
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Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ RuL2017 Serial (down) 2896
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Author German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez
Title Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA Type Book Chapter
Year 2017 Publication Domain Adaptation in Computer Vision Applications Abbreviated Journal
Volume 12 Issue Pages 227-241
Keywords SYNTHIA; Virtual worlds; Autonomous Driving
Abstract Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation.
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Publisher Springer Place of Publication Editor Gabriela Csurka
Language Summary Language Original Title
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Notes ADAS; 600.085; 600.082; 600.076; 600.118 Approved no
Call Number ADAS @ adas @ RSV2017 Serial (down) 2882
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Author David Geronimo; David Vazquez; Arturo de la Escalera
Title Vision-Based Advanced Driver Assistance Systems Type Book Chapter
Year 2017 Publication Computer Vision in Vehicle Technology: Land, Sea, and Air Abbreviated Journal
Volume Issue Pages
Keywords ADAS; Autonomous Driving
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Notes ADAS; 600.118 Approved no
Call Number ADAS @ adas @ GVE2017 Serial (down) 2881
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Author Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros
Title From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example Type Book Chapter
Year 2017 Publication Domain Adaptation in Computer Vision Applications Abbreviated Journal
Volume Issue 13 Pages 243-258
Keywords Domain Adaptation
Abstract Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world.
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Corporate Author Thesis
Publisher Springer Place of Publication Editor Gabriela Csurka
Language Summary Language Original Title
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Notes ADAS; 600.085; 601.223; 600.076; 600.118 Approved no
Call Number ADAS @ adas @ LXG2017 Serial (down) 2872
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Author Pedro Herruzo; Marc Bolaños; Petia Radeva
Title Can a CNN Recognize Catalan Diet? Type Book Chapter
Year 2016 Publication AIP Conference Proceedings Abbreviated Journal
Volume 1773 Issue Pages
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Abstract CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes.
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Notes MILAB Approved no
Call Number Admin @ si @ HBR2016 Serial (down) 2837
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Author Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel
Title An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres Type Book Chapter
Year 2015 Publication The White Book on Public Library Network from Diputació de Barcelona Abbreviated Journal
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Notes MV; DAG;SIAI Approved no
Call Number Admin @ si @VKC2015 Serial (down) 2798
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Author E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva
Title Regularized Clustering for Egocentric Video Segmentation Type Book Chapter
Year 2015 Publication Pattern Recognition and Image Analysis Abbreviated Journal
Volume Issue Pages 327-336
Keywords Temporal video segmentation ; Egocentric videos ; Clustering
Abstract In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.
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Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-19390-8 Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @TDB2015a Serial (down) 2781
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Author Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig
Title Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis Type Book Chapter
Year 2015 Publication Artificial Intelligence Research and Development Abbreviated Journal
Volume 277 Issue Pages 247 - 256
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Abstract Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms.
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Publisher IOS Press Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Frontiers in Artificial Intelligence and Applications Abbreviated Series Title
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Notes MILAB Approved no
Call Number Admin @ si @TVR2015 Serial (down) 2780
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