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Author Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce
Title The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces Type Journal
Year 2018 Publication Technology Innovation Management Review Abbreviated Journal
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Notes DAG; MV; 600.097; 600.121; 600.129;SIAI Approved no
Call Number Admin @ si @ VKV2018a Serial 3153
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Author Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya
Title Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” Type Journal
Year 2018 Publication Informaciones Psiquiatricas Abbreviated Journal
Volume 232 Issue Pages 47-59
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ISSN 0210-7279 ISBN Medium
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Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ FAA2018 Serial 3214
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Author Marçal Rusiñol; Lluis Gomez
Title Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos Type Journal
Year 2018 Publication Revista anual de la Asociación de Archiveros de Castilla y León Abbreviated Journal
Volume 21 Issue Pages 161-174
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Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ RuG2018 Serial 3239
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Author A. Pujol; Juan J. Villanueva
Title A supervised Modification of the Hausdorff distance for visual shape classification Type Journal
Year 2002 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal
Volume 16 Issue 3 Pages 349-359
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Abstract (up) (IF: 0.359)
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Notes ISE Approved no
Call Number PuV2002 Serial 273
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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 (up) 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.
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Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ HJG2015 Serial 2730
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Author V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich
Title Adaptive Correlation Filters for Pattern Recognition Type Journal
Year 2006 Publication Pattern Recognition and Image Analysis Abbreviated Journal
Volume 16 Issue 3 Pages 425-431
Keywords Pattern recognition, Correlation filters, A adaptive filters
Abstract (up) Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.
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Notes ISE Approved no
Call Number ISE @ ise @ KMA2006a Serial 673
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Author Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez
Title Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System Type Journal
Year 2009 Publication Pattern Recognition and Image Analysis Abbreviated Journal
Volume 19 Issue 1 Pages 165-171
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Abstract (up) An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.
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ISSN 1054-6618 ISBN Medium
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Notes ISE Approved no
Call Number ISE @ ise @ MAR2009a Serial 1160
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Author Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner
Title Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation Type Journal
Year 2011 Publication e-Perimetron Abbreviated Journal ePER
Volume 6 Issue 4 Pages 219-229
Keywords
Abstract (up) By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput.
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Notes DAG Approved no
Call Number Admin @ si @ RRL2011a Serial 1765
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Author Mariella Dimiccoli
Title Fundamentals of cone regression Type Journal
Year 2016 Publication Journal of Statistics Surveys Abbreviated Journal
Volume 10 Issue Pages 53-99
Keywords cone regression; linear complementarity problems; proximal operators.
Abstract (up) Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution.
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ISSN 1935-7516 ISBN Medium
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Notes MILAB; Approved no
Call Number Admin @ si @Dim2016a Serial 2783
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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 (up) 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.
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Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ PuR2004a Serial 505
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Author Maria Salamo; Inmaculada Rodriguez; Maite Lopez; Anna Puig; Simone Balocco; Mariona Taule
Title Recurso docente para la atención de la diversidad en el aula mediante la predicción de notas Type Journal
Year 2016 Publication ReVision Abbreviated Journal
Volume 9 Issue 1 Pages
Keywords Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente
Abstract (up) Desde la implantación del Espacio Europeo de Educación Superior (EEES) en los diferentes grados, se ha puesto de manifiesto la necesidad de utilizar diversos mecanismos que permitan tratar la diversidad en el aula, evaluando automáticamente y proporcionando una retroalimentación rápida tanto al alumnado como al profesorado sobre la evolución de los alumnos en una asignatura. En este artículo se presenta la evaluación de la exactitud en las predicciones de GRADEFORESEER, un recurso docente para la predicción de notas basado en técnicas de aprendizaje automático que permite evaluar la evolución del alumnado y estimar su nota final al terminar el curso. Este recurso se ha complementado con una interfaz de usuario para el profesorado que puede ser usada en diferentes plataformas software (sistemas operativos) y en cualquier asignatura de un grado en la que se utilice evaluación continuada. Además de la descripción del recurso, este artículo presenta los resultados obtenidos al aplicar el sistema de predicción en cuatro asignaturas de disciplinas distintas: Programación I (PI), Diseño de Software (DSW) del grado de Ingeniería Informática, Tecnologías de la Información y la Comunicación (TIC) del grado de Lingüística y la asignatura Fundamentos de Tecnología (FDT) del grado de Información y Documentación, todas ellas impartidas en la Universidad de Barcelona.

La capacidad predictiva se ha evaluado de forma binaria (aprueba o no) y según un criterio de rango (suspenso, aprobado, notable o sobresaliente), obteniendo mejores predicciones en los resultados evaluados de forma binaria.
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Notes MILAB; Approved no
Call Number Admin @ si @ SRL2016 Serial 2820
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Author Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer
Title Development of general‐purpose projection‐based augmented reality systems Type Journal
Year 2016 Publication IADIs international journal on computer science and information systems Abbreviated Journal IADIs
Volume 11 Issue 2 Pages 1-18
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Abstract (up) Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups
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Notes DAG; 600.084 Approved no
Call Number Admin @ si @ SCK2016 Serial 2890
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Author Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov
Title Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks Type Journal
Year 2018 Publication Journal of Medical Robotics Research Abbreviated Journal JMRR
Volume 3 Issue 2 Pages
Keywords convolutional neural networks; colonoscopy; computer aided diagnosis
Abstract (up) Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance.
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Notes MV; no menciona Approved no
Call Number BZM2018 Serial 2976
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Author Diego Velazquez; Pau Rodriguez; Alexandre Lacoste; Issam H. Laradji; Xavier Roca; Jordi Gonzalez
Title Evaluating Counterfactual Explainers Type Journal
Year 2023 Publication Transactions on Machine Learning Research Abbreviated Journal TMLR
Volume Issue Pages
Keywords Explainability; Counterfactuals; XAI
Abstract (up) Explainability methods have been widely used to provide insight into the decisions made by statistical models, thus facilitating their adoption in various domains within the industry. Counterfactual explanation methods aim to improve our understanding of a model by perturbing samples in a way that would alter its response in an unexpected manner. This information is helpful for users and for machine learning practitioners to understand and improve their models. Given the value provided by counterfactual explanations, there is a growing interest in the research community to investigate and propose new methods. However, we identify two issues that could hinder the progress in this field. (1) Existing metrics do not accurately reflect the value of an explainability method for the users. (2) Comparisons between methods are usually performed with datasets like CelebA, where images are annotated with attributes that do not fully describe them and with subjective attributes such as ``Attractive''. In this work, we address these problems by proposing an evaluation method with a principled metric to evaluate and compare different counterfactual explanation methods. The evaluation method is based on a synthetic dataset where images are fully described by their annotated attributes. As a result, we are able to perform a fair comparison of multiple explainability methods in the recent literature, obtaining insights about their performance. We make the code public for the benefit of the research community.
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Notes ISE Approved no
Call Number Admin @ si @ VRL2023 Serial 3891
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Author Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li
Title CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing Type Journal
Year 2020 Publication IEEE Transactions on Biometrics, Behavior, and Identity Science Abbreviated Journal TTBIS
Volume 2 Issue 2 Pages 182 - 193
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Abstract (up) Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ ZLW2020 Serial 3412
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