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	Author	Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari
	Title	Objects as context for detecting their semantic parts			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	6907 - 6916
	Keywords	Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection
	Abstract	We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.
	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; 600.120			Approved	no
	Call Number	Admin @ si @ GMF2018			Serial	3229
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	Author	Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio
	Title	Image-to-image translation for cross-domain disentanglement			Type	Conference Article
	Year	2018	Publication	32nd Annual Conference on Neural Information Processing Systems	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	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	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ GWB2018			Serial	3155
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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	Adrien Gaidon; Antonio Lopez; Florent Perronnin
	Title	The Reasonable Effectiveness of Synthetic Visual Data			Type	Journal Article
	Year	2018	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	126	Issue	9	Pages	899–901
	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	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ GLP2018			Serial	3180
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	Author	Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez
	Title	Monocular Depth Estimation by Learning from Heterogeneous Datasets			Type	Conference Article
	Year	2018	Publication	IEEE Intelligent Vehicles Symposium	Abbreviated Journal
	Volume		Issue		Pages	2176 - 2181
	Keywords
	Abstract	Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation.
	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	IV
	Notes	ADAS; 600.124; 600.116; 600.118			Approved	no
	Call Number	Admin @ si @ GUH2018			Serial	3183
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	Author	Albert Clapes; Alex Pardo; Oriol Pujol; Sergio Escalera
	Title	Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly			Type	Journal Article
	Year	2018	Publication	Machine Vision and Applications	Abbreviated Journal	MVAP
	Volume	29	Issue	5	Pages	765–788
	Keywords	Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology
	Abstract	We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach.
	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	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ CPP2018			Serial	3125
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	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
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	Author	Alejandro Cartas; Estefania Talavera; Petia Radeva; Mariella Dimiccoli
	Title	On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams			Type	Miscellaneous
	Year	2018	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users.
	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 @ CTR2018			Serial	3184
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	Author	Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli
	Title	Batch-based activity recognition from egocentric photo-streams revisited			Type	Journal Article
	Year	2018	Publication	Pattern Analysis and Applications	Abbreviated Journal	PAA
	Volume	21	Issue	4	Pages	953–965
	Keywords	Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks
	Abstract	Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.
	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 @ CMR2018			Serial	3186
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	Author	Alicia Fornes; Bart Lamiroy
	Title	Graphics Recognition, Current Trends and Evolutions			Type	Book Whole
	Year	2018	Publication	Graphics Recognition, Current Trends and Evolutions	Abbreviated Journal
	Volume	11009	Issue		Pages
	Keywords
	Abstract	This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017. The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.
	Address
	Corporate Author				Thesis
	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	978-3-030-02283-9	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ FoL2018			Serial	3171
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	Author	Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal
	Title	Results of the use of Kahoot! gamification tool in a course of Chemistry			Type	Conference Article
	Year	2018	Publication	4th International Conference on Higher Education Advances	Abbreviated Journal
	Volume		Issue		Pages	1215-1222
	Keywords
	Abstract	The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
	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 @ ABN2018			Serial	3246
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	Author	Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas
	Title	Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings			Type	Conference Article
	Year	2018	Publication	International Workshop on Reproducible Research in Pattern Recognition	Abbreviated Journal
	Volume	11455	Issue		Pages	71-82
	Keywords
	Abstract	Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
	Address
	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
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ NDC2018			Serial	3178
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	Author	Anjan Dutta; Hichem Sahbi
	Title	Stochastic Graphlet Embedding			Type	Journal Article
	Year	2018	Publication	IEEE Transactions on Neural Networks and Learning Systems	Abbreviated Journal	TNNLS
	Volume		Issue		Pages	1-14
	Keywords	Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality
	Abstract	Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
	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; 602.167; 602.168; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ DuS2018			Serial	3225
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	Author	Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal
	Title	Product graph-based higher order contextual similarities for inexact subgraph matching			Type	Journal Article
	Year	2018	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	76	Issue		Pages	596-611
	Keywords
	Abstract	Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.
	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; 602.167; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ DLB2018			Serial	3083
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	Author	Antonio Lopez
	Title	Pedestrian Detection Systems			Type	Book Chapter
	Year	2018	Publication	Wiley Encyclopedia of Electrical and Electronics Engineering	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning.
	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; 600.118			Approved	no
	Call Number	Admin @ si @ Lop2018			Serial	3230
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