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	Author	David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich
	Title	Traffic sign recognition for computer vision project-based learning			Type	Journal Article
	Year	2013	Publication	IEEE Transactions on Education	Abbreviated Journal	T-EDUC
	Volume	56	Issue	3	Pages	364-371
	Keywords	traffic signs
	Abstract	This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback.
	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	0018-9359	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; CIC			Approved	no
	Call Number	Admin @ si @ GSL2013; ADAS @ adas @			Serial	2160
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	Author	Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin
	Title	Mathematics learning opportunities when playing a Tower Defense Game			Type	Journal
	Year	2015	Publication	International Journal of Serious Games	Abbreviated Journal	IJSG
	Volume	2	Issue	4	Pages	57-71
	Keywords	Tower Defense game; learning opportunities; mathematics; problem solving; game design
	Abstract	A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.
	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	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ HJG2015			Serial	2730
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	Author	Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim
	Title	Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2636 - 2645
	Keywords	Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling
	Abstract	In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.
	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	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ YGS2018			Serial	3115
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	Author	Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso
	Title	Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation			Type	Journal Article
	Year	2017	Publication	Journal of Thoracic Oncology	Abbreviated Journal	JTO
	Volume	12	Issue	1S	Pages	S596-S597
	Keywords	Thorax CT; diagnosis; Peripheral Pulmonary Nodule
	Abstract	A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP).
	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	IAM; 600.096; 600.075; 600.145			Approved	no
	Call Number	Admin @ si @ DGC2017a			Serial	2883
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	Author	Anders Skaarup Johansen; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund
	Title	Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images			Type	Journal Article
	Year	2023	Publication	Applied Sciences	Abbreviated Journal	AS
	Volume	13	Issue	18	Pages
	Keywords	thermal; object detection; concept drift; conditioning; weather recognition
	Abstract	Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system.
	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			Approved	no
	Call Number	Admin @ si @ SNE2023			Serial	3983
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	Author	Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla
	Title	Multi-Image Super-Resolution for Thermal Images			Type	Conference Article
	Year	2022	Publication	17th International Conference on Computer Vision Theory and Applications (VISAPP 2022)	Abbreviated Journal
	Volume	4	Issue		Pages	635-642
	Keywords	Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block
	Abstract	This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches.
	Address	Online; Feb 6-8, 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	VISAPP
	Notes	MSIAU; 601.349			Approved	no
	Call Number	Admin @ si @ RSV2022a			Serial	3690
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	Author	Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution			Type	Journal Article
	Year	2022	Publication	Sensors	Abbreviated Journal	SENS
	Volume	22	Issue	6	Pages	2254
	Keywords	Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images
	Abstract	This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.
	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	MSIAU;			Approved	no
	Call Number	Admin @ si @ RSV2022b			Serial	3688
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	Author	Oriol Pujol; Petia Radeva
	Title	Texture Segmentation by Statistical Deformable Models			Type	Journal
	Year	2004	Publication	International Journal of Image and Graphics	Abbreviated Journal	IJIG
	Volume	4	Issue	3	Pages	433-452
	Keywords	Texture segmentation, parametric active contours, statistic snakes
	Abstract	Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.
	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;HuPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ PuR2004a			Serial	505
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	Author	Bojana Gajic; Eduard Vazquez; Ramon Baldrich
	Title	Evaluation of Deep Image Descriptors for Texture Retrieval			Type	Conference Article
	Year	2017	Publication	Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017)	Abbreviated Journal
	Volume		Issue		Pages	251-257
	Keywords	Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation
	Abstract	The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature. Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures.
	Address	Porto, Portugal; 27 February – 1 March 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	VISIGRAPP
	Notes	CIC; 600.087			Approved	no
	Call Number	Admin @ si @			Serial	3710
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	Author	Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone
	Title	Towards Modelling an Attention-Based Text Localization Process			Type	Conference Article
	Year	2013	Publication	6th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	7887	Issue		Pages	296-303
	Keywords	text localization; visual attention; eye guidance
	Abstract	This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms. Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented.
	Address	Madeira; Portugal; June 2013
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-38627-5	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ CKL2013			Serial	2291
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	Author	Christophe Rigaud; Dimosthenis Karatzas; Joost Van de Weijer; Jean-Christophe Burie; Jean-Marc Ogier
	Title	Automatic text localisation in scanned comic books			Type	Conference Article
	Year	2013	Publication	Proceedings of the International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	814-819
	Keywords	Text localization; comics; text/graphic separation; complex background; unstructured document
	Abstract	Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, an essential step towards a fully automatic comics book understanding. We focus on speech text as it is semantically important and represents the majority of the text present in comics. The approach is compared with existing methods of text localization found in the literature and results are presented.
	Address	Barcelona; February 2013
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	VISAPP
	Notes	DAG; CIC; 600.056			Approved	no
	Call Number	Admin @ si @ RKW2013b			Serial	2261
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	Author	David Fernandez; Josep Llados; Alicia Fornes
	Title	A graph-based approach for segmenting touching lines in historical handwritten documents			Type	Journal Article
	Year	2014	Publication	International Journal on Document Analysis and Recognition	Abbreviated Journal	IJDAR
	Volume	17	Issue	3	Pages	293-312
	Keywords	Text line segmentation; Handwritten documents; Document image processing; Historical document analysis
	Abstract	Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1433-2833	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.056; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ FLF2014			Serial	2459
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	Author	David Aldavert; Marçal Rusiñol
	Title	Manuscript text line detection and segmentation using second-order derivatives analysis			Type	Conference Article
	Year	2018	Publication	13th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	293 - 298
	Keywords	text line detection; text line segmentation; text region detection; second-order derivatives
	Abstract	In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
	Address	Viena; Austria; April 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	DAS
	Notes	DAG; 600.084; 600.129; 302.065; 600.121			Approved	no
	Call Number	Admin @ si @ AlR2018a			Serial	3104
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	Author	Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras
	Title	Segmentation of aerial images for plausible detail synthesis			Type	Journal Article
	Year	2018	Publication	Computers & Graphics	Abbreviated Journal	CG
	Volume	71	Issue		Pages	23-34
	Keywords	Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation
	Abstract	The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.
	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	0097-8493	ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU; 600.086; 600.118			Approved	no
	Call Number	Admin @ si @ ACC2018			Serial	3147
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	Author	Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva
	Title	R-clustering for egocentric video segmentation			Type	Conference Article
	Year	2015	Publication	Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015	Abbreviated Journal
	Volume	9117	Issue		Pages	327-336
	Keywords	Temporal video segmentation; Egocentric videos; Clustering
	Abstract	In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.
	Address	Santiago de Compostela; España; June 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-19389-2	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ TDB2015			Serial	2597
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