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	Author	Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva
	Title	Learning Multi-Subset of Classes for Fine-Grained Food Recognition			Type	Conference Article
	Year	2022	Publication	7th International Workshop on Multimedia Assisted Dietary Management	Abbreviated Journal
	Volume		Issue		Pages	17–26
	Keywords
	Abstract	Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets.
	Address	Lisboa; Portugal; October 2022
	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	MADiMa
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ RNB2022			Serial	3797
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	Author	Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li
	Title	Face Presentation Attack Detection (PAD) Challenges			Type	Book Chapter
	Year	2023	Publication	Advances in Face Presentation Attack Detection	Abbreviated Journal
	Volume		Issue		Pages	17–35
	Keywords
	Abstract	In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	SLCV
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ WGE2023b			Serial	3956
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	Author	Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone
	Title	On the Combination of Ridgelets Descriptors for Symbol Recognition			Type	Conference Article
	Year	2007	Publication	Seventh IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	18–20
	Keywords
	Abstract
	Address	Curitiba (Brazil)
	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	GREC
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RVT2007			Serial	886
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	Author	Luca Ginanni Corradini; Simone Balocco; Luciano Maresca; Silvio Vitale; Matteo Stefanini
	Title	Anatomical Modifications After Stent Implantation: A Comparative Analysis Between CGuard, Wallstent, and Roadsaver Carotid Stents			Type	Journal Article
	Year	2023	Publication	Journal of Endovascular Therapy	Abbreviated Journal
	Volume	30	Issue	1	Pages	18-24
	Keywords	Ginanni Corradini L, Balocco S, Maresca L, Vitale S, Stefanini M.
	Abstract	Abstract Purpose: Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS). Materials and Methods: The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression. Results: For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS. Conclusion: Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents. Level of Evidence: Level 4, Case Series.
	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	xxx			Approved	no
	Call Number	Admin @ si @ GBM2023			Serial	4006
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	Author	Antonio Clavelli; Dimosthenis Karatzas; Josep Llados
	Title	A framework for the assessment of text extraction algorithms on complex colour images			Type	Conference Article
	Year	2010	Publication	9th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	19–26
	Keywords
	Abstract	The availability of open, ground-truthed datasets and clear performance metrics is a crucial factor in the development of an application domain. The domain of colour text image analysis (real scenes, Web and spam images, scanned colour documents) has traditionally suffered from a lack of a comprehensive performance evaluation framework. Such a framework is extremely difficult to specify, and corresponding pixel-level accurate information tedious to define. In this paper we discuss the challenges and technical issues associated with developing such a framework. Then, we describe a complete framework for the evaluation of text extraction methods at multiple levels, provide a detailed ground-truth specification and present a case study on how this framework can be used in a real-life situation.
	Address	Boston; USA;
	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	978-1-60558-773-8	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ CKL2010			Serial	1432
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	Author	Pierluigi Casale; Oriol Pujol; Petia Radeva
	Title	Classyfing Agitation in Sedated ICU Patients			Type	Conference Article
	Year	2010	Publication	Medical Image Computing in Catalunya: Graduate Student Workshop	Abbreviated Journal
	Volume		Issue		Pages	19–20
	Keywords
	Abstract	Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances.
	Address	Girona
	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	MICCAT
	Notes	MILAB;HUPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ COR2010			Serial	1467
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	Author	Carolina Malagelada; F.De Lorio; Fernando Azpiroz; Santiago Segui; Petia Radeva; Anna Accarino; J.Santos; Juan R. Malagelada
	Title	Intestinal Dysmotility in Patients with Functional Intestinal Disorders Demonstrated by Computer Vision Analysis of Capsule Endoscopy Images			Type	Conference Article
	Year	2010	Publication	18th United European Gastroenterology Week	Abbreviated Journal
	Volume	56	Issue	3	Pages	A19-20
	Keywords
	Abstract
	Address	Barcelona
	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	UEGW
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ MLA2010			Serial	1779
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	Author	Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro
	Title	Non-Verbal Communication Analysis in Victim-Offender Mediations			Type	Journal Article
	Year	2015	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	67	Issue	1	Pages	19-27
	Keywords	Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning
	Abstract	We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals.
	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;MV			Approved	no
	Call Number	Admin @ si @ PEP2015			Serial	2583
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation			Type	Conference Article
	Year	2021	Publication	28th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages	19-22
	Keywords
	Abstract	This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
	Address	Anchorage-Alaska; USA; September 2021
	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	ICIP
	Notes	MSIAU; 600.130; 600.122; 601.349			Approved	no
	Call Number	Admin @ si @ SSV2021b			Serial	3579
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	Author	Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
	Title	Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes			Type	Journal
	Year	2013	Publication	Komputer Sapiens	Abbreviated Journal	KS
	Volume	1	Issue		Pages	20-25
	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	OR;MV			Approved	no
	Call Number	Admin @ si @ TSR2013			Serial	2231
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	Author	Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol
	Title	La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals			Type	Journal
	Year	2016	Publication	Lligall, Revista Catalana d'Arxivística	Abbreviated Journal
	Volume	39	Issue		Pages	20-46
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.097			Approved	no
	Call Number	Admin @ si @ FLR2016			Serial	2897
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	Author	Ariel Amato; Angel Sappa; Alicia Fornes; Felipe Lumbreras; Josep Llados
	Title	Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform			Type	Conference Article
	Year	2013	Publication	2nd International ACM Workshop on Crowdsourcing for Multimedia	Abbreviated Journal
	Volume		Issue		Pages	21-22
	Keywords
	Abstract	In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
	Address	Barcelona; October 2013
	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	978-1-4503-2396-3	Medium
	Area		Expedition		Conference	CrowdMM
	Notes	ADAS; ISE; DAG; 600.054; 600.055; 600.045; 600.061; 602.006			Approved	no
	Call Number	Admin @ si @ SLA2013			Serial	2335
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	Author	Sergio Escalera
	Title	Multi-Modal Human Behaviour Analysis from Visual Data Sources			Type	Journal
	Year	2013	Publication	ERCIM News journal	Abbreviated Journal	ERCIM
	Volume	95	Issue		Pages	21-22
	Keywords
	Abstract	The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction.
	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	0926-4981	ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ Esc2013			Serial	2361
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	Author	Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva
	Title	On the Design of Low Redundancy Error-Correcting Output Codes			Type	Book Chapter
	Year	2011	Publication	Ensembles in Machine Learning Applications	Abbreviated Journal
	Volume	373	Issue	2	Pages	21-38
	Keywords
	Abstract	The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.
	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	1860-949X	ISBN	978-3-642-22909-1	Medium
	Area		Expedition		Conference
	Notes	MILAB; OR;HuPBA;MV			Approved	no
	Call Number	Admin @ si @ BEB2011b			Serial	1886
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	Author	Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig
	Title	SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks.			Type	Conference Article
	Year	2018	Publication	21st International Conference on Medical Image Computing & Computer Assisted Intervention	Abbreviated Journal
	Volume	2	Issue		Pages	21-29
	Keywords
	Abstract	Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU.
	Address	Granada; Espanya; September 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	MICCAI
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ SRA2018			Serial	3112
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