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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	Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos
	Title	Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer			Type	Conference Article
	Year	2017	Publication	18th World Conference on Lung Cancer	Abbreviated Journal
	Volume		Issue		Pages
	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	Yokohama; Japan; October 2017
	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	IASLC WCLC
	Notes	IAM; 600.096; 600.075; 600.145			Approved	no
	Call Number	Admin @ si @ OGM2017			Serial	3044
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	Author	Yaxing Wang; Hector Laria Mantecon; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu
	Title	TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets			Type	Conference Article
	Year	2021	Publication	19th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	13990-13999
	Keywords
	Abstract	Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points.
	Address	Virtual; October 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	ICCV
	Notes	LAMP; 600.147; 602.200; 600.120			Approved	no
	Call Number	Admin @ si @ WLW2021			Serial	3604
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	Author	Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui
	Title	Generalized Source-free Domain Adaptation			Type	Conference Article
	Year	2021	Publication	19th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	8958-8967
	Keywords
	Abstract	Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains.
	Address	Virtual; October 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
	Notes	LAMP; 600.120; 600.147			Approved	no
	Call Number	Admin @ si @ YWW2021			Serial	3605
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	Author	Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera
	Title	DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation			Type	Conference Article
	Year	2021	Publication	19th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	5471-5480
	Keywords
	Abstract	We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.
	Address	Virtual; October 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	ICCV
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ BMT2021			Serial	3606
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	Author	Ekaterina Zaytseva; Jordi Vitria
	Title	A search based approach to non maximum suppression in face detection			Type	Conference Article
	Year	2012	Publication	19th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Poster paper TA.P5.12 Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results.
	Address	Orlando; USA; September 2012
	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	1522-4880	ISBN	978-1-4673-2534-9	Medium
	Area		Expedition		Conference	ICIP
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ ZaV2012			Serial	2060
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	Author	G. de Oliveira; Mariella Dimiccoli; Petia Radeva
	Title	Egocentric Image Retrieval With Deep Convolutional Neural Networks			Type	Conference Article
	Year	2016	Publication	19th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages	71-76
	Keywords
	Abstract
	Address	Barcelona; Spain; October 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	CCIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ODR2016			Serial	2790
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	Author	Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi
	Title	Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light			Type	Conference Article
	Year	2016	Publication	19th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus
	Abstract	Automated video and image analysis can be a very efficient tool to analyze animal behavior based on sociality, especially in hard access environments for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior. This emergent behavior is modulated by the day-night cycle, but their social interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community.
	Address	Barcelona; Spain; October 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	CCIA
	Notes	OR;MV;			Approved	no
	Call Number	Admin @ si @ GMS2016			Serial	2816
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	Author	Petia Radeva
	Title	Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better?			Type	Conference Article
	Year	2016	Publication	19th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume	4	Issue		Pages
	Keywords
	Abstract
	Address	Barcelona; October 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	CCIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ Rad2016			Serial	2832
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	Author	Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer
	Title	When Deep Learners Change Their Mind: Learning Dynamics for Active Learning			Type	Conference Article
	Year	2021	Publication	19th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	13052	Issue	1	Pages	403-413
	Keywords
	Abstract	Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
	Address	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	CAIP
	Notes	LAMP; OR			Approved	no
	Call Number	Admin @ si @ ZRV2021			Serial	3673
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	Author	Mikhail Mozerov; Ariel Amato; Xavier Roca
	Title	Occlusion Handling in Trinocular Stereo using Composite Disparity Space Image			Type	Conference Article
	Year	2009	Publication	19th International Conference on Computer Graphics and Vision	Abbreviated Journal
	Volume		Issue		Pages	69–73
	Keywords
	Abstract	In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) can be merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. A dense disparity map is obtained with a global optimization algorithm using the proposed composite DSI. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. One of the top positions in the rank of the Middlebury website confirms the performance of our method to be competitive with the best stereo matching.
	Address	Moscow (Russia)
	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	978-5-317-02975-3	Medium
	Area		Expedition		Conference	GRAPHICON
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ MAR2009b			Serial	1207
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla
	Title	Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture			Type	Conference Article
	Year	2017	Publication	19th international conference on image analysis and processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	CNN in Multispectral Imaging; Image Colorization
	Abstract	This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results.
	Address	Catania; Italy; September 2017
	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	ICIAP
	Notes	ADAS; MSIAU; 600.086; 600.122; 600.118			Approved	no
	Call Number	Admin @ si @ SSV2017c			Serial	3016
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	Author	Jose Marone; Simone Balocco; Marc Bolaños; Jose Massa; Petia Radeva
	Title	Learning the Lumen Border using a Convolutional Neural Networks classiﬁer			Type	Conference Article
	Year	2016	Publication	19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows.
	Address	Athens; Greece; October 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	MICCAIW
	Notes	MILAB;			Approved	no
	Call Number	Admin @ si @ MBB2016			Serial	2822
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	Author	Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	Stable Anatomical Structure Tracking for video-bronchoscopy Navigation			Type	Conference Article
	Year	2016	Publication	19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking
	Abstract	Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at dierent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations.
	Address	Athens; Greece; October 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	MICCAIW
	Notes	IAM; 600.075			Approved	no
	Call Number	Admin @ si @ LSB2016b			Serial	2857
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	Author	Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell
	Title	Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches			Type	Conference Article
	Year	2016	Publication	19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops	Abbreviated Journal
	Volume	9401	Issue		Pages	62-70
	Keywords	Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy
	Abstract	Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface.
	Address	Quebec; Canada; September 2016
	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	MICCAIW
	Notes	IAM; MV; 600.060; 600.075			Approved	no
	Call Number	Admin @ si @ SGB2016			Serial	2885
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