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	Author	Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell
	Title	High-Level Clothes Description Based on Colour-Texture and Structural Features			Type	Conference Article
	Year	2003	Publication	1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Palma de Mallorca
	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;CIC			Approved	no
	Call Number	CAT @ cat @ BTL2003b			Serial	369
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	Author	Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell
	Title	High-Level Clothes Description Based on Color-Texture and Structural Features			Type	Book Chapter
	Year	2003	Publication	Lecture Notes in Computer Science	Abbreviated Journal
	Volume	2652	Issue		Pages	108–116
	Keywords
	Abstract	This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images.
	Address	Springer-Verlag
	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;CIC			Approved	no
	Call Number	CAT @ cat @ BTL2003a			Serial	368
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	Author	Agnes Borras; Josep Llados
	Title	Corest: A measure of color and space stability to detect salient regions according to human criteria			Type	Conference Article
	Year	2009	Publication	5th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	204-209
	Keywords
	Abstract
	Address	Lisboa, Portugal
	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	978-989-8111-69-2	Medium
	Area		Expedition		Conference	VISAPP
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ BoL2009			Serial	1225
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	Author	Agnes Borras; Josep Llados
	Title	A Multi-Scale Layout Descriptor Based on Delaunay Triangulation for Image Retrieval			Type	Conference Article
	Year	2008	Publication	3rd International Conference on Computer Vision Theory and Applications VISAPP (2) 2008	Abbreviated Journal
	Volume	2	Issue		Pages	139-144
	Keywords
	Abstract
	Address	Funchal, Madeira (Portugal)
	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	DAG @ dag @ BoL2008			Serial	981
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	Author	Agnes Borras; Josep Llados
	Title	Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination			Type	Book Chapter
	Year	2007	Publication	3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120	Abbreviated Journal	LNCS
	Volume	4478	Issue		Pages	33–39
	Keywords
	Abstract	This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications.
	Address	Girona (Spain)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-540-72848-1	Medium
	Area		Expedition		Conference
	Notes	DAG;			Approved	no
	Call Number	DAG @ dag @ BoL2007a; IAM @ iam @ BoL2007a			Serial	776
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	Author	Agnes Borras; Josep Llados
	Title	Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints			Type	Book Chapter
	Year	2005	Publication	Pattern Recognition And Image Analysis	Abbreviated Journal	LNCS
	Volume	3522	Issue		Pages	325–332
	Keywords
	Abstract	This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling.
	Address	Estoril (Portugal)
	Corporate Author				Thesis
	Publisher	Springer Link	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	DAG @ dag @ BoL2005; IAM @ iam @ BoL2005			Serial	556
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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	Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Polyxeni Gkontra; Karim Lekadir; Gloria Menegaz; Petia Radeva
	Title	A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation			Type	Conference Article
	Year	2021	Publication	34th International Symposium on Computer-Based Medical Systems	Abbreviated Journal
	Volume		Issue		Pages	492-497
	Keywords
	Abstract	Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy.
	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	CBMS
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ SBZ2021			Serial	3629
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	Author	Ahmed Mounir Gad
	Title	Object Localization Enhancement by Multiple Segmentation Fusion			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	152	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Master's 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 @ Mou2010			Serial	1346
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	Author	Ahmed Salih; Ilaria Boscolo Galazzo; Zahra Raisi Estabragh; Steffen E Petersen; Gloria Menegaz; Petia Radeva
	Title	Characterizing the contribution of dependent features in XAI methods			Type	Miscellaneous
	Year	2023	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparent. In this context, many XAI methods were proposed being SHAP and LIME the most popular. However, the proposed methods assume that used predictors in the machine learning models are independent which in general is not necessarily true. Such assumption casts shadows on the robustness of the XAI outcomes such as the list of informative predictors. Here, we propose a simple, yet useful proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the predictors. The proposed approach has the advantage of being model-agnostic as well as simple to calculate the impact of each predictor in the model in presence of collinearity.
	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			Approved	no
	Call Number	Admin @ si @ SBR2023			Serial	3868
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	Author	Aitor Alvarez-Gila
	Title	Self-supervised learning for image-to-image translation in the small data regime			Type	Book Whole
	Year	2022	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Computer vision; Neural networks; Self-supervised learning; Image-to-image mapping; Probabilistic programming
	Abstract	The mass irruption of Deep Convolutional Neural Networks (CNNs) in computer vision since 2012 led to a dominance of the image understanding paradigm consisting in an end-to-end fully supervised learning workflow over large-scale annotated datasets. This approach proved to be extremely useful at solving a myriad of classic and new computer vision tasks with unprecedented performance —often, surpassing that of humans—, at the expense of vast amounts of human-labeled data, extensive computational resources and the disposal of all of our prior knowledge on the task at hand. Even though simple transfer learning methods, such as fine-tuning, have achieved remarkable impact, their success when the amount of labeled data in the target domain is small is limited. Furthermore, the non-static nature of data generation sources will often derive in data distribution shifts that degrade the performance of deployed models. As a consequence, there is a growing demand for methods that can exploit elements of prior knowledge and sources of information other than the manually generated ground truth annotations of the images during the network training process, so that they can adapt to new domains that constitute, if not a small data regime, at least a small labeled data regime. This thesis targets such few or no labeled data scenario in three distinct image-to-image mapping learning problems. It contributes with various approaches that leverage our previous knowledge of different elements of the image formation process: We first present a data-efficient framework for both defocus and motion blur detection, based on a model able to produce realistic synthetic local degradations. The framework comprises a self-supervised, a weakly-supervised and a semi-supervised instantiation, depending on the absence or availability and the nature of human annotations, and outperforms fully-supervised counterparts in a variety of settings. Our knowledge on color image formation is then used to gather input and target ground truth image pairs for the RGB to hyperspectral image reconstruction task. We make use of a CNN to tackle this problem, which, for the first time, allows us to exploit spatial context and achieve state-of-the-art results given a limited hyperspectral image set. In our last contribution to the subfield of data-efficient image-to-image transformation problems, we present the novel semi-supervised task of zero-pair cross-view semantic segmentation: we consider the case of relocation of the camera in an end-to-end trained and deployed monocular, fixed-view semantic segmentation system often found in industry. Under the assumption that we are allowed to obtain an additional set of synchronized but unlabeled image pairs of new scenes from both original and new camera poses, we present ZPCVNet, a model and training procedure that enables the production of dense semantic predictions in either source or target views at inference time. The lack of existing suitable public datasets to develop this approach led us to the creation of MVMO, a large-scale Multi-View, Multi-Object path-traced dataset with per-view semantic segmentation annotations. We expect MVMO to propel future research in the exciting under-developed fields of cross-view and multi-view semantic segmentation. Last, in a piece of applied research of direct application in the context of process monitoring of an Electric Arc Furnace (EAF) in a steelmaking plant, we also consider the problem of simultaneously estimating the temperature and spectral emissivity of distant hot emissive samples. To that end, we design our own capturing device, which integrates three point spectrometers covering a wide range of the Ultra-Violet, visible, and Infra-Red spectra and is capable of registering the radiance signal incoming from an 8cm diameter spot located up to 20m away. We then define a physically accurate radiative transfer model that comprises the effects of atmospheric absorbance, of the optical system transfer function, and of the sample temperature and spectral emissivity themselves. We solve this inverse problem without the need for annotated data using a probabilistic programming-based Bayesian approach, which yields full posterior distribution estimates of the involved variables that are consistent with laboratory-grade measurements.
	Address	Julu, 2019
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Joost Van de Weijer; Estibaliz Garrote
	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 @ Alv2022			Serial	3716
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	Author	Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer
	Title	Self-supervised blur detection from synthetically blurred scenes			Type	Journal Article
	Year	2019	Publication	Image and Vision Computing	Abbreviated Journal	IMAVIS
	Volume	92	Issue		Pages	103804
	Keywords
	Abstract	Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.
	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; 600.120			Approved	no
	Call Number	Admin @ si @ AGG2019			Serial	3301
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	Author	Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote
	Title	Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB			Type	Conference Article
	Year	2017	Publication	1st International Workshop on Physics Based Vision meets Deep Learning	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset.
	Address	Venice; Italy; 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	ICCV-PBDL
	Notes	LAMP; 600.109; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ AWG2017			Serial	2969
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	Author	Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote
	Title	MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation			Type	Conference Article
	Year	2022	Publication	29th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	multi-view; cross-view; semantic segmentation; synthetic dataset
	Abstract	We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 .
	Address	Bordeaux; France; October2022
	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	LAMP			Approved	no
	Call Number	Admin @ si @ AWW2022			Serial	3781
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	Author	Ajian Liu; Chenxu Zhao; Zitong Yu; Anyang Su; Xing Liu; Zijian Kong; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Guodong Guo
	Title	3D High-Fidelity Mask Face Presentation Attack Detection Challenge			Type	Conference Article
	Year	2021	Publication	IEEE/CVF International Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	814-823
	Keywords
	Abstract	The threat of 3D mask to face recognition systems is increasing serious, and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask based attack detection. It attracted more than 200 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranked algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.
	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	ICCVW
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ LZY2021			Serial	3646
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