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	Author	Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester
	Title	A computational model of amodal completion			Type	Conference Article
	Year	2016	Publication	SIAM Conference on Imaging Science	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
	Address	Albuquerque; New Mexico; USA; May 2016
	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	IS
	Notes	MILAB; 601.235			Approved	no
	Call Number	Admin @ si @OHD2016a			Serial	2788
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	Author	G. de Oliveira; A. Cartas; Marc Bolaños; Mariella Dimiccoli; Xavier Giro; Petia Radeva
	Title	LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task			Type	Conference Article
	Year	2016	Publication	12th NTCIR Conference on Evaluation of Information Access Technologies	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.
	Address	Tokyo; Japan; June 2016
	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	NTCIR
	Notes	MILAB;			Approved	no
	Call Number	Admin @ si @OCB2016			Serial	2789
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	Author	G. de Oliveira; Mariella Dimiccoli; Petia Radeva
	Title	Egocentric Image Retrieval With Deep Convolutional Neural Networks			Type	Conference Article
	Year	2016	Publication	19th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages	71-76
	Keywords
	Abstract
	Address	Barcelona; Spain; October 2016
	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	CCIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ODR2016			Serial	2790
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	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	With whom do I interact with? Social interaction detection in egocentric photo-streams			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
	Address	Cancun; Mexico; December 2016
	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	MILAB			Approved	no
	Call Number	Admin @ si @ADR2016a			Serial	2791
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	Author	Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
	Title	Dynamic Lexicon Generation for Natural Scene Images			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	395-410
	Keywords	scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN
	Abstract	Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge benet from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed. In this paper we propose a method that generates contextualized lexicons for scene images using only visual information. For this, we exploit the correlation between visual and textual information in a dataset consisting of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline.
	Address	Amsterdam; The Netherlands; October 2016
	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; 600.084			Approved	no
	Call Number	Admin @ si @ PGR2016			Serial	2825
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	Author	Fernando Vilariño; Dan Norton; Onur Ferhat
	Title	The Eye Doesn't Click – Eyetracking and Digital Content Interaction			Type	Conference Article
	Year	2016	Publication	4S/EASST Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona; Spain; September 2016
	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	EASST
	Notes	MV; 600.097;SIAI			Approved	no
	Call Number	Admin @ si @VNF2016			Serial	2801
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	Author	Fernando Vilariño
	Title	Giving Value to digital collections in the Public Library			Type	Conference Article
	Year	2016	Publication	Librarian 2020	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Brussels; Belgium; October 2016
	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	LIB
	Notes	MV; 600.097;SIAI			Approved	no
	Call Number	Admin @ si @Vil2016a			Serial	2802
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	Author	Fernando Vilariño; Dimosthenis Karatzas
	Title	A Living Lab approach for Citizen Science in Libraries			Type	Conference Article
	Year	2016	Publication	1st International ECSA Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Berlin; Germany; May 2016
	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	ECSA
	Notes	MV; DAG; 600.084; 600.097;SIAI			Approved	no
	Call Number	Admin @ si @ViK2016			Serial	2804
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	Author	Fernando Vilariño
	Title	Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation			Type	Conference Article
	Year	2016	Publication	International Workshop on Poetry: Archives, Poetries and Receptions	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona; Spain; October 2016
	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	POETRY
	Notes	MV; 600.097;SIAI			Approved	no
	Call Number	Admin @ si @Vil2016b			Serial	2805
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	Author	Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira
	Title	Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives			Type	Journal Article
	Year	2016	Publication	Robotics and Autonomous Systems	Abbreviated Journal	RAS
	Volume	83	Issue		Pages	312-325
	Keywords	Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives
	Abstract	When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier B.V.	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	ADAS; 600.086, 600.076			Approved	no
	Call Number	Admin @ si @OSS2016a			Serial	2806
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	Author	Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla
	Title	Wavelet based visible and infrared image fusion: a comparative study			Type	Journal Article
	Year	2016	Publication	Sensors	Abbreviated Journal	SENS
	Volume	16	Issue	6	Pages	1-15
	Keywords	Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform
	Abstract	This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).
	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	ADAS; 600.086; 600.076			Approved	no
	Call Number	Admin @ si @SCA2016			Serial	2807
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	Author	Victor Ponce
	Title	Evolutionary Bags of Space-Time Features for Human Analysis			Type	Book Whole
	Year	2016	Publication	PhD Thesis Universitat de Barcelona, UOC and CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture
	Abstract	The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice
	Address	June 2016
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Sergio Escalera;Xavier Baro;Hugo Jair Escalante
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA			Approved	no
	Call Number	Pon2016			Serial	2814
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	Author	Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo
	Title	Learning cross-spectral similarity measures with deep convolutional neural networks			Type	Conference Article
	Year	2016	Publication	29th IEEE Conference on Computer Vision and Pattern Recognition Worshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.
	Address	Las vegas; USA; June 2016
	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	ADAS; 600.086; 600.076			Approved	no
	Call Number	Admin @ si @AAS2016			Serial	2809
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	Author	Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo
	Title	Monocular visual odometry: A cross-spectral image fusion based approach			Type	Journal Article
	Year	2016	Publication	Robotics and Autonomous Systems	Abbreviated Journal	RAS
	Volume	85	Issue		Pages	26-36
	Keywords	Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion
	Abstract	This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier B.V.	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	ADAS;600.086; 600.076			Approved	no
	Call Number	Admin @ si @SAC2016			Serial	2811
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	Author	Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera
	Title	ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Behavior Analysis; Personality Traits; First Impressions
	Abstract	This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge.
	Address	Amsterdam; The Netherlands; October 2016
	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	HuPBA;MV; 600.063			Approved	no
	Call Number	Admin @ si @ PCP2016			Serial	2828
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