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	Author	Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce
	Title	Libraries as New Innovation Hubs: The Library Living Lab			Type	Conference Article
	Year	2018	Publication	30th ISPIM Innovation Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation.
	Address	Stockholm; May 2018
	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	ISPIM
	Notes	DAG; MV; 600.097; 600.121; 600.129;SIAI			Approved	no
	Call Number	Admin @ si @ VKV2018b			Serial	3154
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	Author	Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy
	Title	End-to-end Driving via Conditional Imitation Learning			Type	Conference Article
	Year	2018	Publication	IEEE International Conference on Robotics and Automation	Abbreviated Journal
	Volume		Issue		Pages	4693 - 4700
	Keywords
	Abstract	Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL
	Address	Brisbane; Australia; May 2018
	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	ICRA
	Notes	ADAS; 600.116; 600.124; 600.118			Approved	no
	Call Number	Admin @ si @ CML2018			Serial	3108
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	Author	Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy
	Title	On Offline Evaluation of Vision-based Driving Models			Type	Conference Article
	Year	2018	Publication	15th European Conference on Computer Vision	Abbreviated Journal
	Volume	11219	Issue		Pages	246-262
	Keywords	Autonomous driving; deep learning
	Abstract	Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics.
	Address	Munich; September 2018
	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	ECCV
	Notes	ADAS; 600.124; 600.118			Approved	no
	Call Number	Admin @ si @ CLK2018			Serial	3162
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	Author	Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma
	Title	Scale coding bag of deep features for human attribute and action recognition			Type	Journal Article
	Year	2018	Publication	Machine Vision and Applications	Abbreviated Journal	MVAP
	Volume	29	Issue	1	Pages	55-71
	Keywords	Action recognition; Attribute recognition; Bag of deep features
	Abstract	Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.
	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	LAMP; 600.068; 600.079; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ KWR2018			Serial	3107
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	Author	F.Negin; Pau Rodriguez; M.Koperski; A.Kerboua; Jordi Gonzalez; J.Bourgeois; E.Chapoulie; P.Robert; F.Bremond
	Title	PRAXIS: Towards automatic cognitive assessment using gesture recognition			Type	Journal Article
	Year	2018	Publication	Expert Systems with Applications	Abbreviated Journal	ESWA
	Volume	106	Issue		Pages	21-35
	Keywords
	Abstract	Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer’s disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults. In order to carry out gesture recognition as well as correctness assessment of the performances we have recollected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames. We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis.
	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	ISE			Approved	no
	Call Number	Admin @ si @ NRK2018			Serial	3669
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	Author	F. Javier Sanchez; Jorge Bernal
	Title	Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject			Type	Conference Article
	Year	2018	Publication	4th International Conference of Higher Education Advances	Abbreviated Journal
	Volume		Issue		Pages	1359-1366
	Keywords	Monitoring; Evaluation tool; Gamification; Student motivation
	Abstract	The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject.
	Address	Valencia; June 2018
	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	HEAD
	Notes	MV; no proj			Approved	no
	Call Number	Admin @ si @ SaB2018			Serial	3165
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	Author	Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya
	Title	Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable”			Type	Journal
	Year	2018	Publication	Informaciones Psiquiatricas	Abbreviated Journal
	Volume	232	Issue		Pages	47-59
	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	0210-7279	ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ FAA2018			Serial	3214
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	Author	Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	BronchoX: bronchoscopy exploration software for biopsy intervention planning			Type	Journal
	Year	2018	Publication	Healthcare Technology Letters	Abbreviated Journal	HTL
	Volume	5	Issue	5	Pages	177–182
	Keywords
	Abstract	Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors’ solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea.
	Address
	Corporate Author	rank (SJR)			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	IAM; 600.096; 600.075; 601.323; 601.337; 600.145			Approved	no
	Call Number	Admin @ si @ RSB2018a			Serial	3132
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	Author	Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy			Type	Conference Article
	Year	2018	Publication	OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis	Abbreviated Journal
	Volume	11041	Issue		Pages
	Keywords	Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification
	Abstract	Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems.
	Address	Granada; September 2018
	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	MICCAIW
	Notes	IAM; 600.096; 600.075; 601.323; 600.145			Approved	no
	Call Number	Admin @ si @ RSB2018b			Serial	3137
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	Author	Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva
	Title	Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants			Type	Journal Article
	Year	2018	Publication	IEEE Transactions on Multimedia	Abbreviated Journal
	Volume	20	Issue	12	Pages	3266 - 3275
	Keywords
	Abstract	The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.
	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; no proj			Approved	no
	Call Number	Admin @ si @ ARB2018			Serial	3236
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	Author	Domicele Jonauskaite; Nele Dael; C. Alejandro Parraga; Laetitia Chevre; Alejandro Garcia Sanchez; Christine Mohr
	Title	Stripping #The Dress: The importance of contextual information on inter-individual differences in colour perception			Type	Journal Article
	Year	2018	Publication	Psychological Research	Abbreviated Journal	PSYCHO R
	Volume		Issue		Pages	1-15
	Keywords
	Abstract	In 2015, a picture of a Dress (henceforth the Dress) triggered popular and scientific interest; some reported seeing the Dress in white and gold (W&G) and others in blue and black (B&B). We aimed to describe the phenomenon and investigate the role of contextualization. Few days after the Dress had appeared on the Internet, we projected it to 240 students on two large screens in the classroom. Participants reported seeing the Dress in B&B (48%), W&G (38%), or blue and brown (B&Br; 7%). Amongst numerous socio-demographic variables, we only observed that W&G viewers were most likely to have always seen the Dress as W&G. In the laboratory, we tested how much contextual information is necessary for the phenomenon to occur. Fifty-seven participants selected colours most precisely matching predominant colours of parts or the full Dress. We presented, in this order, small squares (a), vertical strips (b), and the full Dress (c). We found that (1) B&B, B&Br, and W&G viewers had selected colours differing in lightness and chroma levels for contextualized images only (b, c conditions) and hue for fully contextualized condition only (c) and (2) B&B viewers selected colours most closely matching displayed colours of the Dress. Thus, the Dress phenomenon emerges due to inter-individual differences in subjectively perceived lightness, chroma, and hue, at least when all aspects of the picture need to be integrated. Our results support the previous conclusions that contextual information is key to colour perception; it should be important to understand how this actually happens.
	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	NEUROBIT; no proj			Approved	no
	Call Number	Admin @ si @ JDP2018			Serial	3149
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	Author	Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou
	Title	The Robust Reading Competition Annotation and Evaluation Platform			Type	Conference Article
	Year	2018	Publication	13th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	61-66
	Keywords
	Abstract	The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished 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	Viena; Austria; April 2018
	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	DAS
	Notes	DAG; 600.084; 600.121			Approved	no
	Call Number	KGR2018			Serial	3103
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	Author	Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov
	Title	Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images			Type	Conference Article
	Year	2018	Publication	International Workshop on Egocentric Perception, Interaction and Computing at ECCV	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.
	Address	Munich; Alemanya; September 2018
	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	ECCVW
	Notes	DAG; 600.129; 600.121;			Approved	no
	Call Number	Admin @ si @ BKB2018b			Serial	3174
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	Author	Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov
	Title	Word Spotting in Scene Images based on Character Recognition			Type	Conference Article
	Year	2018	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	1872-1874
	Keywords
	Abstract	In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.
	Address	Salt Lake City; USA; June 2018
	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	CVPRW
	Notes	DAG; 600.129; 600.121			Approved	no
	Call Number	BKB2018a			Serial	3179
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	Author	Dena Bazazian
	Title	Fully Convolutional Networks for Text Understanding in Scene Images			Type	Book Whole
	Year	2018	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task. The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging.
	Address	November 2018
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Dimosthenis Karatzas;Andrew Bagdanov
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-948531-1-1	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Baz2018			Serial	3220
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