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	Author	Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim
	Title	Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2636 - 2645
	Keywords	Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling
	Abstract	In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.
	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	CVPR
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ YGS2018			Serial	3115
Permanent link to this record



	Author	Albert Clapes; Ozan Bilici; Dariia Temirova; Egils Avots; Gholamreza Anbarjafari; Sergio Escalera
	Title	From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation			Type	Conference Article
	Year	2018	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	2373-2382
	Keywords
	Abstract
	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	HUPBA			Approved	no
	Call Number	Admin @ si @			Serial	3116
Permanent link to this record



	Author	Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi
	Title	Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures			Type	Conference Article
	Year	2018	Publication	13th IEEE Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages	250 - 257
	Keywords
	Abstract	Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate.
	Address	Xian; China; 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	FG
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ HBN2018			Serial	3117
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	Author	Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari
	Title	Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification			Type	Conference Article
	Year	2018	Publication	8th International Workshop on Human Behavior Understanding	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.
	Address	Xian; China; 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	FGW
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ HKI2018			Serial	3118
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	Author	Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari
	Title	Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context			Type	Conference Article
	Year	2018	Publication	IEEE Winter Applications of Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	28-36
	Keywords	IEEE Winter Applications of Computer Vision Workshops
	Abstract	Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.
	Address	Lake Tahoe; USA; March 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	WACVW
	Notes	HUPBA; 602.133			Approved	no
	Call Number	Admin @ si @ JBA2018			Serial	3121
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	Author	Cesar de Souza
	Title	Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video			Type	Book Whole
	Year	2018	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos.
	Address	April 2018
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Antonio Lopez;Naila Murray
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ Sou2018			Serial	3127
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	Author	Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell
	Title	Color-based data augmentation for Reflectance Estimation			Type	Conference Article
	Year	2018	Publication	26th Color Imaging Conference	Abbreviated Journal
	Volume		Issue		Pages	284-289
	Keywords
	Abstract	Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods.
	Address	Vancouver; November 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	CIC
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ SSB2018a			Serial	3129
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	Author	Yaxing Wang; Joost Van de Weijer; Luis Herranz
	Title	Mix and match networks: encoder-decoder alignment for zero-pair image translation			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5467 - 5476
	Keywords
	Abstract	We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.
	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	CVPR
	Notes	LAMP; 600.109; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ WWH2018b			Serial	3131
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	Author	Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste
	Title	TADAM: Task dependent adaptive metric for improved few-shot learning			Type	Conference Article
	Year	2018	Publication	32nd Annual Conference on Neural Information Processing Systems	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100.
	Address	Montreal; Canada; December 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	NIPS
	Notes	ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ ORL2018			Serial	3140
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	Author	Mohammed Al Rawi; Dimosthenis Karatzas
	Title	On the Labeling Correctness in Computer Vision Datasets			Type	Conference Article
	Year	2018	Publication	Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Image datasets have heavily been used to build computer vision systems. These datasets are either manually or automatically labeled, which is a problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble.
	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	ECML-PKDDW
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ RaK2018			Serial	3144
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	Author	Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio
	Title	On the Duality Between Retinex and Image Dehazing			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	8212–8221
	Keywords	Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting
	Abstract	Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.
	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	CVPR
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ GAB2018			Serial	3146
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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	Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados
	Title	Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework			Type	Conference Article
	Year	2018	Publication	14th Asian Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.
	Address	Perth; Australia; December 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	ACCV
	Notes	DAG; 600.097; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DDG2018a			Serial	3151
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	Author	Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal
	Title	Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	916 - 921
	Keywords
	Abstract	In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.
	Address	Beijing; China; August 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	ICPR
	Notes	DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DDG2018b			Serial	3152
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	Author	Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce
	Title	The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces			Type	Journal
	Year	2018	Publication	Technology Innovation Management Review	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; MV; 600.097; 600.121; 600.129;SIAI			Approved	no
	Call Number	Admin @ si @ VKV2018a			Serial	3153
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