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	Author	Angel Sappa; Fadi Dornaika
	Title	An Edge-Based Approach to Motion Detection			Type	Book Chapter
	Year	2006	Publication	6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Reading (United Kingdom)
	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			Approved	no
	Call Number	ADAS @ adas @ SaD2006			Serial	654
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	Author	Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera
	Title	Multi-varied Cumulative Alignment for Domain Adaptation			Type	Conference Article
	Year	2022	Publication	6th International Conference on Image Analysis and Processing	Abbreviated Journal
	Volume	13232	Issue		Pages	324–334
	Keywords	Domain Adaptation; Computer vision; Neural networks
	Abstract	Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca).
	Address	Indonesia; October 2022
	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	ICIAP
	Notes	HuPBA; no menciona			Approved	no
	Call Number	Admin @ si @ OAS2022			Serial	3777
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	Author	Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy			Type	Conference Article
	Year	2015	Publication	6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015	Abbreviated Journal
	Volume	10	Issue	6	Pages	935-945
	Keywords
	Abstract	PURPOSE: Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service. METHODS: Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion. RESULTS: Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room. CONCLUSIONS: Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.
	Address	Barcelona; Spain; June 2015
	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	IPCAI
	Notes	IAM; MV; 600.075			Approved	no
	Call Number	Admin @ si @ SBS2015b			Serial	2613
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	Author	Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto
	Title	Va-ID: A trust-based virtual assessment system			Type	Conference Article
	Year	2014	Publication	6th International Conference on Intelligent Networking and Collaborative Systems	Abbreviated Journal
	Volume		Issue		Pages	328 - 335
	Keywords
	Abstract	Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model.
	Address	Salerna; Italy; September 2014
	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	978-1-4799-6386-7	Medium
	Area		Expedition		Conference	INCOS
	Notes	OR; HuPBA;MV			Approved	no
	Call Number	Admin @ si @ ABB2014			Serial	2620
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	Author	Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen
	Title	Combining Holistic and Part-based Deep Representations for Computational Painting Categorization			Type	Conference Article
	Year	2016	Publication	6th International Conference on Multimedia Retrieval	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification.
	Address	New York; 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	ICMR
	Notes	LAMP; 600.068; 600.079;ADAS			Approved	no
	Call Number	Admin @ si @ RKW2016			Serial	2763
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	Author	W. Liu; Josep Llados
	Title	Graphics Recognition. Ten Years Review and Future Perspectives			Type	Book Whole
	Year	2006	Publication	6th International Workshop	Abbreviated Journal
	Volume	3926	Issue		Pages
	Keywords
	Abstract
	Address	Hong Kong (China)
	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	GREC
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ LiL2006			Serial	800
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	Author	Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika
	Title	Multi-observation Face Recognition in Videos based on Label Propagation			Type	Conference Article
	Year	2015	Publication	6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015	Abbreviated Journal
	Volume		Issue		Pages	10-17
	Keywords
	Abstract	In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods.
	Address	Boston; USA; June 2015
	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	LAMP; 600.068; 600.072;			Approved	no
	Call Number	Admin @ si @ RBD2015			Serial	2627
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	Author	Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell
	Title	Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer			Type	Conference Article
	Year	2017	Publication	6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging	Abbreviated Journal
	Volume	10550	Issue		Pages	151-159
	Keywords
	Abstract	Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy. The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%. We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
	Address	Quebec; Canada; September 2017
	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	CLIP
	Notes	IAM; 600.096; 600.075; 600.145			Approved	no
	Call Number	Admin @ si @ GRM2017			Serial	2957
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	Author	Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
	Title	Unsupervised Deep Feature Extraction Of Hyperspectral Images			Type	Conference Article
	Year	2014	Publication	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification
	Abstract	This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features.
	Address	Lausanne; Switzerland; June 2014
	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	WHISPERS
	Notes	MILAB; LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ RGC2014			Serial	2513
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	Author	Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa
	Title	Color Naming for Multi-Color Fashion Items			Type	Conference Article
	Year	2018	Publication	6th World Conference on Information Systems and Technologies	Abbreviated Journal
	Volume	747	Issue		Pages	64-73
	Keywords	Deep learning; Color; Multi-label
	Abstract	There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results.
	Address	Naples; 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	WORLDCIST
	Notes	LAMP; 600.109; 601.309; 600.120			Approved	no
	Call Number	Admin @ si @ YWR2018			Serial	3161
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	Author	Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva
	Title	Situation Graph Trees for Human Behavior Modeling			Type	Miscellaneous
	Year	2004	Publication	7th Catalan Conference for Artificial Intelligence (CCIA’2004)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona (Spain)
	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	ISE @ ise @ GVR2004b			Serial	498
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	Author	Albert Clapes; Miguel Reyes; Sergio Escalera
	Title	User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis			Type	Conference Article
	Year	2012	Publication	7th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume	7378	Issue		Pages	1-11
	Keywords
	Abstract	We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
	Address	Mallorca
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-31566-4	Medium
	Area		Expedition		Conference	AMDO
	Notes	HUPBA;MILAB			Approved	no
	Call Number	Admin @ si @ CRE2012			Serial	2010
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	Author	Wenjuan Gong; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca
	Title	A New Image Dataset on Human Interactions			Type	Conference Article
	Year	2012	Publication	7th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume	7378	Issue		Pages	204-209
	Keywords
	Abstract	This article describes a new collection of still image dataset which are dedicated to interactions between people. Human action recognition from still images have been a hot topic recently, but most of them are actions performed by a single person, like running, walking, riding bikes, phoning and so on and there is no interactions between people in one image. The dataset collected in this paper are concentrating on human interaction between two people aiming to explore this new topic in the research area of action recognition from still images.
	Address	Mallorca
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-31566-4	Medium
	Area		Expedition		Conference	AMDO
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ GGT2012			Serial	2030
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	Author	Sergio Escalera
	Title	Human Behavior Analysis From Depth Maps			Type	Conference Article
	Year	2012	Publication	7th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume	7378	Issue		Pages	282-292
	Keywords
	Abstract	Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed.
	Address	Mallorca
	Corporate Author				Thesis
	Publisher	Springer Heidelberg	Place of Publication		Editor	F.J. Perales; R.B. Fisher; T.B. Moeslund
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-31566-4	Medium
	Area		Expedition		Conference	AMDO
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ Esc2012			Serial	2040
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	Author	Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; Horst Bunke
	Title	A Recursive Embedding Approach to Median Graph Computation			Type	Conference Article
	Year	2009	Publication	7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition	Abbreviated Journal
	Volume	5534	Issue		Pages	113–123
	Keywords
	Abstract	The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches.
	Address	Venice, Italy
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-02123-7	Medium
	Area		Expedition		Conference	GBR
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ FKV2009			Serial	1173
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