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	Author	E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva
	Title	Regularized Clustering for Egocentric Video Segmentation			Type	Book Chapter
	Year	2015	Publication	Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume		Issue		Pages	327-336
	Keywords	Temporal video segmentation ; Egocentric videos ; Clustering
	Abstract	In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.
	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	978-3-319-19390-8	Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @TDB2015a			Serial	2781
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	Author	Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo
	Title	Subspace Procrustes Analysis			Type	Journal Article
	Year	2017	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	121	Issue	3	Pages	327–343
	Keywords
	Abstract	Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our 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	MILAB; HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ PTI2017			Serial	2841
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	Author	Joan Serrat; Felipe Lumbreras; Idoia Ruiz
	Title	Learning to measure for preshipment garment sizing			Type	Journal Article
	Year	2018	Publication	Measurement	Abbreviated Journal	MEASURE
	Volume	130	Issue		Pages	327-339
	Keywords	Apparel; Computer vision; Structured prediction; Regression
	Abstract	Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.
	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; MSIAU; 600.122; 600.118			Approved	no
	Call Number	Admin @ si @ SLR2018			Serial	3128
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	Author	Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
	Title	Automatic Verification of Properly Signed Multi-page Document Images			Type	Conference Article
	Year	2015	Publication	Proceedings of the Eleventh International Symposium on Visual Computing	Abbreviated Journal
	Volume	9475	Issue		Pages	327-336
	Keywords	Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow
	Abstract	In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages.
	Address	Las Vegas, Nevada, USA; December 2015
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume	9475	Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ISVC
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @			Serial	3189
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	Author	Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio
	Title	Deriving global quantitative tumor response parameters from 18F-FDG PET-CT scans in patients with non-Hodgkins lymphoma			Type	Journal Article
	Year	2015	Publication	Nuclear Medicine Communications	Abbreviated Journal	NMC
	Volume	36	Issue	4	Pages	328-333
	Keywords
	Abstract	OBJECTIVES: The aim of the study was to address the need for quantifying the global cancer time evolution magnitude from a pair of time-consecutive positron emission tomography-computed tomography (PET-CT) scans. In particular, we focus on the computation of indicators using image-processing techniques that seek to model non-Hodgkin's lymphoma (NHL) progression or response severity. MATERIALS AND METHODS: A total of 89 pairs of time-consecutive PET-CT scans from NHL patients were stored in a nuclear medicine station for subsequent analysis. These were classified by a consensus of nuclear medicine physicians into progressions, partial responses, mixed responses, complete responses, and relapses. The cases of each group were ordered by magnitude following visual analysis. Thereafter, a set of quantitative indicators designed to model the cancer evolution magnitude within each group were computed using semiautomatic and automatic image-processing techniques. Performance evaluation of the proposed indicators was measured by a correlation analysis with the expert-based visual analysis. RESULTS: The set of proposed indicators achieved Pearson's correlation results in each group with respect to the expert-based visual analysis: 80.2% in progressions, 77.1% in partial response, 68.3% in mixed response, 88.5% in complete response, and 100% in relapse. In the progression and mixed response groups, the proposed indicators outperformed the common indicators used in clinical practice [changes in metabolic tumor volume, mean, maximum, peak standardized uptake value (SUV mean, SUV max, SUV peak), and total lesion glycolysis] by more than 40%. CONCLUSION: Computing global indicators of NHL response using PET-CT imaging techniques offers a strong correlation with the associated expert-based visual analysis, motivating the future incorporation of such quantitative and highly observer-independent indicators in oncological decision making or treatment response evaluation scenarios.
	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;MILAB			Approved	no
	Call Number	Admin @ si @ SDE2015			Serial	2605
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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	Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
	Title	Who Painted this Painting?			Type	Conference Article
	Year	2010	Publication	Proceedings of The CREATE 2010 Conference	Abbreviated Journal
	Volume		Issue		Pages	329–333
	Keywords
	Abstract
	Address	Gjovik (Norway)
	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	CREATE
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ KWV2010			Serial	1329
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	Author	Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai
	Title	Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks			Type	Conference Article
	Year	2022	Publication	17th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume	13804	Issue		Pages	329–344
	Keywords
	Abstract	Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection.
	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	978-3-031-25068-2	Medium
	Area		Expedition		Conference	ECCV-TiE
	Notes	DAG; 600.162; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ GBC2022			Serial	3795
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	Author	Oscar Camara; Estanislao Oubel; Gemma Piella; Simone Balocco; Mathieu De Craene; Alejandro F. Frangi
	Title	Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging			Type	Conference Article
	Year	2009	Publication	5th International Conference on Functional Imaging and Modeling of the Heart	Abbreviated Journal
	Volume	5528	Issue		Pages	330–338
	Keywords
	Abstract	In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm).
	Address	Nice, France
	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	978-3-642-01931-9	Medium
	Area		Expedition		Conference	FIMH
	Notes	MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ COP2009			Serial	1255
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	Author	Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus
	Title	Mobile eHealth Platform for Home Monitoring of Bipolar Disorder			Type	Conference Article
	Year	2021	Publication	27th ACM International Conference on Multimedia Modeling	Abbreviated Journal
	Volume	12573	Issue		Pages	330-341
	Keywords
	Abstract	People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur. MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.
	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	MMM
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ CEE2021			Serial	3659
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	Author	Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Using the Average Landmark Vector Method for Robot Homing			Type	Conference Article
	Year	2007	Publication	Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA	Abbreviated Journal
	Volume	163	Issue		Pages	331–338
	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	978–1–58603–798–7	Medium
	Area		Expedition		Conference	CCIA’07
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ GRL2007			Serial	899
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	Author	Joan Oliver; Ricardo Toledo; J. Pujol; J. Sorribes; E. Valderrama
	Title	Un ABP basado en la robotica para las ingenierias informaticas			Type	Miscellaneous
	Year	2009	Publication	15th Jornadas de Enseñanza Universitaria de la Informatica	Abbreviated Journal
	Volume		Issue		Pages	331–338
	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	ISBN:978–84–692–2758–9	Medium
	Area		Expedition		Conference	JENUI
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ OTP2009			Serial	1253
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	Author	Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
	Title	Multimodal page classification in administrative document image streams			Type	Journal Article
	Year	2014	Publication	International Journal on Document Analysis and Recognition	Abbreviated Journal	IJDAR
	Volume	17	Issue	4	Pages	331-341
	Keywords	Digital mail room; Multimodal page classification; Visual and textual document description
	Abstract	In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
	Address
	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	1433-2833	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079			Approved	no
	Call Number	Admin @ si @ RFK2014			Serial	2523
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	Author	Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate
	Title	Feature Extraction by Using Dual-Generalized Discriminative Common Vectors			Type	Journal Article
	Year	2019	Publication	Journal of Mathematical Imaging and Vision	Abbreviated Journal	JMIV
	Volume	61	Issue	3	Pages	331-351
	Keywords	Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning
	Abstract	In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.
	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; ADAS; 600.084; 600.118; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DRR2019			Serial	3172
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	Author	Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov
	Title	Variable Rate Deep Image Compression with Modulated Autoencoder			Type	Journal Article
	Year	2020	Publication	IEEE Signal Processing Letters	Abbreviated Journal	SPL
	Volume	27	Issue		Pages	331-335
	Keywords
	Abstract	Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; ADAS; 600.141; 600.120; 600.118			Approved	no
	Call Number	Admin @ si @ YHW2020			Serial	3346
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