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	Author	Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez
	Title	Invertible conditional gans for image editing			Type	Conference Article
	Year	2016	Publication	30th Annual Conference on Neural Information Processing Systems Worshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.
	Address	Barcelona; Spain; December 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	NIPSW
	Notes	LAMP; ADAS; 600.068			Approved	no
	Call Number	Admin @ si @ PWR2016			Serial	2906
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	Author	Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva
	Title	Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods			Type	Conference Article
	Year	2022	Publication	29th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring
	Abstract	Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans.
	Address	Bordeaux; France; 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	ICIP
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ SBC2022			Serial	3789
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	Author	Chenyang Fu; Kaida Xiao; Dimosthenis Karatzas; Sophie Wuerger
	Title	Investigation of Unique Hue Setting Changes with Ageing			Type	Journal Article
	Year	2011	Publication	Chinese Optics Letters	Abbreviated Journal	COL
	Volume	9	Issue	5	Pages	053301-1-5
	Keywords
	Abstract	Clromatic sensitivity along the protan, deutan, and tritan lines and the loci of the unique hues (red, green, yellow, blue) for a very large sample (n = 185) of colour-normal observers ranging from 18 to 75 years of age are assessed. Visual judgments are obtained under normal viewing conditions using colour patches on self-luminous display under controlled adaptation conditions. Trivector discrimination thresholds show an increase as a function of age along the protan, deutan, and tritan axes, with the largest increase present along the tritan line, less pronounced shifts in unique hue settings are also observed. Based on the chromatic (protan, deutan, tritan) thresholds and using scaled cone signals, we predict the unique hue changes with ageing. A dependency on age for unique red and unique yellow for predicted hue angle is found. We conclude that the chromatic sensitivity deteriorates significantly with age, whereas the appearance of unique hues is much less affected, remaining almost constant despite the known changes in the ocular media.
	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			Approved	no
	Call Number	Admin @ si @ XFW2011			Serial	1818
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	Author	Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas
	Title	Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching			Type	Conference Article
	Year	2022	Publication	Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	1391-1400
	Keywords	Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning
	Abstract	The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin.
	Address	Virtual; Waikoloa; Hawai; USA; January 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	WACV
	Notes	DAG; 600.155; 302.105;			Approved	no
	Call Number	Admin @ si @ BMG2022			Serial	3663
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	Author	Zhijie Fang; Antonio Lopez
	Title	Is the Pedestrian going to Cross? Answering by 2D Pose Estimation			Type	Conference Article
	Year	2018	Publication	IEEE Intelligent Vehicles Symposium	Abbreviated Journal
	Volume		Issue		Pages	1271 - 1276
	Keywords
	Abstract	Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results.
	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	IV
	Notes	ADAS; 600.124; 600.116; 600.118			Approved	no
	Call Number	Admin @ si @ FaL2018			Serial	3181
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	Author	Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
	Title	Is there a pattern of Chromosome territoriality along mice spermatogenesis?			Type	Conference Article
	Year	2017	Publication	3rd Spanish MeioNet Meeting Abstract Book	Abbreviated Journal
	Volume		Issue		Pages	55-56
	Keywords
	Abstract
	Address	Miraflores de la Sierra; Madrid; June 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	MEIONET
	Notes	IAM; 600.096; 600.145			Approved	no
	Call Number	Admin @ si @			Serial	2958
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	Author	Frederic Sampedro; Sergio Escalera; Anna Puig
	Title	Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation			Type	Journal Article
	Year	2014	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	46	Issue		Pages	1-10
	Keywords	Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation
	Abstract	In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation 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 @ SEP2014			Serial	2550
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	Author	Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin
	Title	Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval			Type	Journal Article
	Year	2012	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	35	Issue	12	Pages	2916-2929
	Keywords
	Abstract	This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.
	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	0162-8828	ISBN	978-1-4577-0394-2	Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GLG 2012b			Serial	2008
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	Author	ChuanMing Fang; Kai Wang; Joost Van de Weijer
	Title	IterInv: Iterative Inversion for Pixel-Level T2I Models			Type	Conference Article
	Year	2023	Publication	37th Annual Conference on Neural Information Processing Systems	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are relying on DDIM inversion as a common practice based on the Latent Diffusion Models (LDM). However, the large pretrained T2I models working on the latent space as LDM suffer from losing details due to the first compression stage with an autoencoder mechanism. Instead, another mainstream T2I pipeline working on the pixel level, such as Imagen and DeepFloyd-IF, avoids this problem. They are commonly composed of several stages, normally with a text-to-image stage followed by several super-resolution stages. In this case, the DDIM inversion is unable to find the initial noise to generate the original image given that the super-resolution diffusion models are not compatible with the DDIM technique. According to our experimental findings, iteratively concatenating the noisy image as the condition is the root of this problem. Based on this observation, we develop an iterative inversion (IterInv) technique for this stream of T2I models and verify IterInv with the open-source DeepFloyd-IF model. By combining our method IterInv with a popular image editing method, we prove the application prospects of IterInv. The code will be released at \url{this https URL}.
	Address	New Orleans; USA; December 2023
	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	NEURIPS
	Notes	LAMP			Approved	no
	Call Number	Admin @ si @ FWW2023			Serial	3936
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	Author	X. Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva
	Title	iTrack: Image-based Probabilistic Tracking of People.			Type	Miscellaneous
	Year	2000	Publication	15 th International Conference on Pattern Recognition, 3:1122–1125.	Abbreviated Journal
	Volume		Issue		Pages
	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
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ VGR2000a			Serial	228
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	Author	Debora Gil; Petia Radeva; Josefina Mauri
	Title	Ivus Segmentation Via a Regularized Curvature Flow			Type	Conference Article
	Year	2002	Publication	X Congreso Anual de la Sociedad Española de Ingeniería Biomédica CASEIB 2002	Abbreviated Journal
	Volume		Issue		Pages	133-136
	Keywords
	Abstract	Cardiac diseases are diagnosed and treated through a study of the morphology and dynamics of cardiac arteries. In- travascular Ultrasound (IVUS) imaging is of high interest to physicians since it provides both information. At the current state-of-the-art in image segmentation, a robust detection of the arterial lumen in IVUS demands manual intervention or ECG-gating. Manual intervention is a tedious and time consuming task that requires experienced observers, meanwhile ECG-gating is an acquisition technique not available in all clinical centers. We introduce a parametric algorithm that detects the arterial luminal border in in vivo sequences. The method consist in smoothing the sequences’ level surfaces under a regularized mean curvature flow that admits non-trivial steady states. The flow is based on a measure of the surface local smoothness that takes into account regularity of the surface curvature.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication	Saragossa, Espanya	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	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ GRM2002			Serial	1536
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	Author	Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva
	Title	IVUS Tissue Characterization with Sub-class Error-correcting Output Codes			Type	Conference Article
	Year	2008	Publication	Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, pp. 1–8, 23–28 juny 2008.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different subsets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers and feature sets.
	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	CVPR
	Notes	MILAB;HuPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ EPM2008			Serial	1041
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	Author	Maya Dimitrova; I. Terziev; Petia Radeva; Juan J. Villanueva
	Title	Java-Servlet Technology for Building New Web Document Classifiers			Type	Miscellaneous
	Year	2004	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Varna (Bulgaria)
	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			Approved	no
	Call Number	BCNPCL @ bcnpcl @ DTR2004			Serial	476
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	Author	Zeynep Yucel; Albert Ali Salah; Çetin Meriçli; Tekin Meriçli; Roberto Valenti; Theo Gevers
	Title	Joint Attention by Gaze Interpolation and Saliency			Type	Journal
	Year	2013	Publication	IEEE Transactions on cybernetics	Abbreviated Journal	T-CIBER
	Volume	43	Issue	3	Pages	829-842
	Keywords
	Abstract	Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.
	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	2168-2267	ISBN		Medium
	Area		Expedition		Conference
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ YSM2013			Serial	2363
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	Author	Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari
	Title	Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases			Type	Conference Article
	Year	2017	Publication	12th IEEE International Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
	Address	Washington; DC; USA; May 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	FG
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ LJG2017			Serial	2924
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