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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 (up) Journal
Year 2022 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal
Volume 13 Issue 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.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKE2022a Serial 3660
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Author Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio
Title A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding Type (up) Journal
Year 2021 Publication SMPTE Motion Imaging Journal Abbreviated Journal SMPTE
Volume 130 Issue 4 Pages 53 - 65
Keywords
Abstract In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components.
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Notes CIC Approved no
Call Number SCV2021 Serial 3671
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Author Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
Title Detailed 3D face reconstruction from a single RGB image Type (up) Journal
Year 2019 Publication Journal of WSCG Abbreviated Journal JWSCG
Volume 27 Issue 2 Pages 103-112
Keywords 3D Wrinkle Reconstruction; Face Analysis, Optimization.
Abstract This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
Address 2019/11
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Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ Serial 3708
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Author Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez
Title Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem Type (up) Journal
Year 2020 Publication Mathematics Abbreviated Journal MATH
Volume 20 Issue 8(9) Pages 1595
Keywords STEM education; Project-based learning; Coding; software tool
Abstract In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view.
Address September 2020
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Notes IAM; ISE Approved no
Call Number Admin @ si @ Serial 3722
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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 (up) Journal
Year 2022 Publication Historical Life Course Studies Abbreviated Journal HLCS
Volume 12 Issue 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
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Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ PFR2022 Serial 3737
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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 (up) Journal
Year 2023 Publication Ingeniare. Revista chilena de ingenieria Abbreviated Journal
Volume 31 Issue 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.
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Notes MILAB Approved no
Call Number Admin @ si @ CAA2023 Serial 3846
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Author Diego Velazquez; Pau Rodriguez; Alexandre Lacoste; Issam H. Laradji; Xavier Roca; Jordi Gonzalez
Title Evaluating Counterfactual Explainers Type (up) Journal
Year 2023 Publication Transactions on Machine Learning Research Abbreviated Journal TMLR
Volume Issue 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.
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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 (up) Journal
Year 2023 Publication Artificial Intelligence in Agriculture Abbreviated Journal
Volume 9 Issue 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.
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Notes MSIAU Approved no
Call Number Admin @ si @ SVC2023 Serial 3892
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Author Petia Radeva; Judit Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva
Title CORKIDENT: an automatic vision system for real-time inspection of natural products. Type (up) Journal Article
Year 1999 Publication Abbreviated Journal
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Address Wales
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ RMT1999 Serial 23
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Author Jordi Vitria; Petia Radeva; X. Binefa
Title EigenHistograms: using low dimensional models of color distribution for real time object recognition Type (up) Journal Article
Year 1999 Publication Abbreviated Journal
Volume Issue Pages
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Address Ljubliana, Slovenia, Springer-Verlag
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VRB1999a Serial 29
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Author D. Rincon; E. Frumento; M. Angel Viñas
Title Description of a teleconsultation platform and its interaction with access networks. Type (up) Journal Article
Year 1999 Publication V Open European Summer School. 145–150. Abbreviated Journal
Volume Issue Pages
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Address Barcelona
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Notes Approved no
Call Number ISE @ ise @ RFV1999 Serial 32
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Author Maria Vanrell; Jordi Vitria; Xavier Roca
Title A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Type (up) Journal Article
Year 1997 Publication Machine Vision and Applications Abbreviated Journal
Volume 9 Issue Pages 262–271
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Notes OR;ISE;CIC;MV Approved no
Call Number BCNPCL @ bcnpcl @ VVR1997 Serial 35
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Author D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom
Title Automated classification of renal interstitium and tubules by local texture analysis and a neural network Type (up) Journal Article
Year 1996 Publication Analytical and Quantitative Cytology and Histology Abbreviated Journal
Volume 18 Issue 5 Pages 410-9, PMID: 8908314
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ SMG1996 Serial 76
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Author Felipe Lumbreras; Joan Serrat
Title Segmentation of petrographical images of marbles Type (up) Journal Article
Year 1996 Publication Computers and Geosciences. 22(5):547–558 Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ LuS1996b Serial 82
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Author F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra
Title Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. Type (up) Journal Article
Year 1994 Publication Kidney International Abbreviated Journal
Volume 46 Issue 6 Pages 1721-1727
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MSV1994 Serial 113
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