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Author Marçal Rusiñol
Title Classificació semàntica i visual de documents digitals Type Journal
Year 2019 Publication Revista de biblioteconomia i documentacio Abbreviated Journal
Volume Issue (up) Pages 75-86
Keywords
Abstract Se analizan los sistemas de procesamiento automático que trabajan sobre documentos digitalizados con el objetivo de describir los contenidos. De esta forma contribuyen a facilitar el acceso, permitir la indización automática y hacer accesibles los documentos a los motores de búsqueda. El objetivo de estas tecnologías es poder entrenar modelos computacionales que sean capaces de clasificar, agrupar o realizar búsquedas sobre documentos digitales. Así, se describen las tareas de clasificación, agrupamiento y búsqueda. Cuando utilizamos tecnologías de inteligencia artificial en los sistemas de
clasificación esperamos que la herramienta nos devuelva etiquetas semánticas; en sistemas de agrupamiento que nos devuelva documentos agrupados en clusters significativos; y en sistemas de búsqueda esperamos que dada una consulta, nos devuelva una lista ordenada de documentos en función de la relevancia. A continuación se da una visión de conjunto de los métodos que nos permiten describir los documentos digitales, tanto de manera visual (cuál es su apariencia), como a partir de sus contenidos semánticos (de qué hablan). En cuanto a la descripción visual de documentos se aborda el estado de la cuestión de las representaciones numéricas de documentos digitalizados
tanto por métodos clásicos como por métodos basados en el aprendizaje profundo (deep learning). Respecto de la descripción semántica de los contenidos se analizan técnicas como el reconocimiento óptico de caracteres (OCR); el cálculo de estadísticas básicas sobre la aparición de las diferentes palabras en un texto (bag-of-words model); y los métodos basados en aprendizaje profundo como el método word2vec, basado en una red neuronal que, dadas unas cuantas palabras de un texto, debe predecir cuál será la
siguiente palabra. Desde el campo de las ingenierías se están transfiriendo conocimientos que se han integrado en productos o servicios en los ámbitos de la archivística, la biblioteconomía, la documentación y las plataformas de gran consumo, sin embargo los algoritmos deben ser lo suficientemente eficientes no sólo para el reconocimiento y transcripción literal sino también para la capacidad de interpretación de los contenidos.
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; 600.084; 600.135; 600.121; 600.129 Approved no
Call Number Admin @ si @ Rus2019 Serial 3282
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Author Xiangyang Li; Luis Herranz; Shuqiang Jiang
Title Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition Type Journal
Year 2020 Publication ACM Transactions on Data Science Abbreviated Journal ACM
Volume Issue (up) Pages
Keywords
Abstract In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.
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; 600.141; 600.120 Approved no
Call Number Admin @ si @ LHJ2020 Serial 3423
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD Type Journal
Year 2022 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal
Volume 13 Issue (up) Pages 591–611
Keywords
Abstract One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKE2022a Serial 3660
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre
Title The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data Type Journal
Year 2022 Publication Historical Life Course Studies Abbreviated Journal HLCS
Volume 12 Issue (up) Pages 99-132
Keywords Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences
Abstract The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography.
Address June 23, 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
Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ PFR2022 Serial 3737
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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 (up) Pages
Keywords Explainability; Counterfactuals; XAI
Abstract 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.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ VRL2023 Serial 3891
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Author Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa
Title Corn kernel classification from few training samples Type Journal
Year 2023 Publication Artificial Intelligence in Agriculture Abbreviated Journal
Volume 9 Issue (up) Pages 89-99
Keywords
Abstract This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.
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 @ SVC2023 Serial 3892
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Author Josep Llados; Dorothea Blostein
Title Special Issue on Graphics Recognition Type Journal
Year 2007 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 9 Issue (up) 1 Pages 1–2
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Guest Editors 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 @ LlB2007 Serial 781
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Author Ignasi Rius; Jordi Gonzalez; Mikhail Mozerov; Xavier Roca
Title Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis Type Journal
Year 2008 Publication International Journal for Computational Vision and Biomechanics Abbreviated Journal
Volume 1 Issue (up) 1 Pages 33–43
Keywords
Abstract
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 ISE Approved no
Call Number ISE @ ise @ RGM2008 Serial 1020
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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 (up) 1 Pages 165-171
Keywords
Abstract 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.
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 1054-6618 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number ISE @ ise @ MAR2009a Serial 1160
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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 (up) 1 Pages
Keywords Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente
Abstract 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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; Approved no
Call Number Admin @ si @ SRL2016 Serial 2820
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Author Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca
Title Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits Type Journal
Year 2019 Publication International Journal of Social Science and Humanity Abbreviated Journal IJSSH
Volume 9 Issue (up) 1 Pages 6-12
Keywords
Abstract Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones.
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 ISE; 600.119 Approved no
Call Number Admin @ si @ RGG2019 Serial 3414
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Author E. Provenzi; Carlo Gatta; M. Fierro; A. Rizzi
Title A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Constant Type Journal
Year 2008 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 30 Issue (up) 10 Pages 1757–1770
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ PGF2008 Serial 1001
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Author Robert Benavente; Maria Vanrell; Ramon Baldrich
Title Parametric Fuzzy Sets for Automatic Color Naming Type Journal
Year 2008 Publication Journal of the Optical Society of America A Abbreviated Journal
Volume 25 Issue (up) 10 Pages 2582–2593
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Abstract
Address
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Series Editor Series Title Abbreviated Series Title
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Notes CIC Approved no
Call Number CAT @ cat @ BVB2008 Serial 1004
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Author Roger Max Calle Quispe; Maya Aghaei Gavari; Eduardo Aguilar Torres
Title Towards real-time accurate safety helmets detection through a deep learning-based method Type Journal
Year 2023 Publication Ingeniare. Revista chilena de ingenieria Abbreviated Journal
Volume 31 Issue (up) 12 Pages
Keywords
Abstract Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.

These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability.
Address
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ CAA2023 Serial 3846
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Author Jordi Vitria; J. Llacer
Title Reconstructing 3D light microscopic images using the EM algorithm Type Journal
Year 1996 Publication Pattern Recognition Letters Abbreviated Journal
Volume 17 Issue (up) 14 Pages 1491–1498
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Abstract
Address
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ ViL1996 Serial 74
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