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	Author	Yaxing Wang; Abel Gonzalez-Garcia; David Berga; Luis Herranz; Fahad Shahbaz Khan; Joost Van de Weijer
	Title	MineGAN: effective knowledge transfer from GANs to target domains with few images			Type	Conference Article
	Year	2020	Publication	33rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.
	Address	Virtual CVPR
	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; 600.141; 600.120			Approved	no
	Call Number	Admin @ si @ WGB2020			Serial	3421
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	Author	Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol
	Title	Minimal Design of Error-Correcting Output Codes			Type	Journal Article
	Year	2011	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	33	Issue	6	Pages	693-702
	Keywords	Multi-class classification; Error-correcting output codes; Ensemble of classifiers
	Abstract	IF JCR CCIA 1.303 2009 54/103 The classification of large number of object categories is a challenging trend in the pattern recognition field. In 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 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 minimal 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 minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0167-8655	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; OR;HuPBA;MV			Approved	no
	Call Number	Admin @ si @ BEB2011a			Serial	1800
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	Author	Victor M. Campello; Carlos Martin-Isla; Cristian Izquierdo; Andrea Guala; Jose F. Rodriguez Palomares; David Vilades; Martin L. Descalzo; Mahir Karakas; Ersin Cavus; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Karim Lekadir
	Title	Minimising multi-centre radiomics variability through image normalisation: a pilot study			Type	Journal Article
	Year	2022	Publication	Scientific Reports	Abbreviated Journal	ScR
	Volume	12	Issue	1	Pages	12532
	Keywords
	Abstract	Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
	Address	2022/07/22
	Corporate Author				Thesis
	Publisher	Springer Nature	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			Approved	no
	Call Number	Admin @ si @ CMI2022			Serial	3749
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	Author	Debora Gil; Agnes Borras; Manuel Ballester; Francesc Carreras; Ruth Aris; Manuel Vazquez; Enric Marti; Ferran Poveda
	Title	MIOCARDIA: Integrating cardiac function and muscular architecture for a better diagnosis			Type	Conference Article
	Year	2011	Publication	14th International Symposium on Applied Sciences in Biomedical and Communication Technologies	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. The MIOCARDIA project is a multidisciplinary project in cooperation with l'Hospital de la Santa Creu i de Sant Pau, Clinica la Creu Blanca and Barcelona Supercomputing Center. The ultimate goal of this project is defining a computational model of the myocardium. The model takes into account the deep interrelation between the anatomy and the mechanics of the heart. The paper explains the workflow of the MIOCARDIA project. It also introduces a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and provides evidences of a global helical organization.
	Address	Barcelona; Spain
	Corporate Author	Association for Computing Machinery			Thesis
	Publisher		Place of Publication	Barcelona, Spain	Editor	Association for Computing Machinery
	Language	english	Summary Language	english	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-0913-4	Medium
	Area		Expedition		Conference	ISABEL
	Notes	IAM			Approved	no
	Call Number	IAM @ iam @ GGB2011			Serial	1691
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	Author	Carme Julia
	Title	Missig Data Matrix Factorization Addressing the Structure from Motion Problem			Type	Miscellaneous
	Year	2008	Publication	CVC–UAB	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bellaterra
	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	978–84–935251–6–3	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ Jul2008			Serial	1104
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	Author	Yaxing Wang; Joost Van de Weijer; Luis Herranz
	Title	Mix and match networks: encoder-decoder alignment for zero-pair image translation			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5467 - 5476
	Keywords
	Abstract	We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.
	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; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ WWH2018b			Serial	3131
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	Author	Yaxing Wang; Luis Herranz; Joost Van de Weijer
	Title	Mix and match networks: multi-domain alignment for unpaired image-to-image translation			Type	Journal Article
	Year	2020	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	128	Issue		Pages	2849–2872
	Keywords
	Abstract	This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities
	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; 600.109; 600.106; 600.141; 600.120			Approved	no
	Call Number	Admin @ si @ WHW2020			Serial	3424
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	Author	Olivier Penacchio
	Title	Mixed Hodge Structures and Equivariant Sheaves on the Projective Plane			Type	Journal Article
	Year	2011	Publication	Mathematische Nachrichten	Abbreviated Journal	MN
	Volume	284	Issue	4	Pages	526-542
	Keywords	Mixed Hodge structures, equivariant sheaves, MSC (2010) Primary: 14C30, Secondary: 14F05, 14M25
	Abstract	We describe an equivalence of categories between the category of mixed Hodge structures and a category of equivariant vector bundles on a toric model of the complex projective plane which verify some semistability condition. We then apply this correspondence to define an invariant which generalizes the notion of R-split mixed Hodge structure and give calculations for the first group of cohomology of possibly non smooth or non-complete curves of genus 0 and 1. Finally, we describe some extension groups of mixed Hodge structures in terms of equivariant extensions of coherent sheaves. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
	Address
	Corporate Author				Thesis
	Publisher	WILEY-VCH Verlag	Place of Publication		Editor	R. Mennicken
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1522-2616	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ Pen2011			Serial	1721
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	Author	Hao Wu; Alejandro Ariza-Casabona; Bartłomiej Twardowski; Tri Kurniawan Wijaya
	Title	MM-GEF: Multi-modal representation meet collaborative filtering			Type	Miscellaneous
	Year	2023	Publication	ARXIV	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation 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	LAMP			Approved	no
	Call Number	Admin @ si @ WAT2023			Serial	3988
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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	Daniel Ponsa; Jordi Vitria
	Title	Mobile monitoring system using an agent-oriented approach			Type	Miscellaneous
	Year	1999	Publication	Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bilbao
	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;OR;MV			Approved	no
	Call Number	ADAS @ adas @ DaV1999			Serial	21
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	Author	Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors			Type	Conference Article
	Year	2008	Publication	IEEE International Conference on Robotics and Automation,	Abbreviated Journal
	Volume		Issue		Pages	538–543
	Keywords
	Abstract
	Address	Pasadena; CA; 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		Medium
	Area		Expedition		Conference	ICRA
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ RTL2008			Serial	1144
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	Author	Cristina Cañero; Nikolaos Thomos; George A. Triantafyllid; George C. Litos; Michael G. Strintzis
	Title	Mobile Tele-echography: User Interface Design			Type	Journal
	Year	2005	Publication	IEEE Transactions on Information Technology in Biomedicine, 9(1):44–49 (IF: 1.376)	Abbreviated Journal
	Volume		Issue		Pages
	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				Approved	no
	Call Number	Admin @ si @ CTT2005			Serial	537
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	Author	Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig
	Title	MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network			Type	Miscellaneous
	Year	2019	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	CoRR abs/1907.00856 Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%.
	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 menciona			Approved	no
	Call Number	Admin @ si @ MRA2019			Serial	3384
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	Author	Emanuel Indermühle; Volkmar Frinken; Horst Bunke
	Title	Mode Detection in Online Handwritten Documents using BLSTM Neural Networks			Type	Conference Article
	Year	2012	Publication	13th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	302-307
	Keywords
	Abstract	Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.
	Address	Bari, italy
	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	978-1-4673-2262-1	Medium
	Area		Expedition		Conference	ICFHR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ IFB2012			Serial	2056
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