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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 (up) 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 Angel Sappa; Boris X. Vintimilla
Title Cost-Based Closed Contour Representations Type Journal
Year 2007 Publication (up) Journal of Electronic Imaging, 16(2), 023009 (9 pages) Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ SaV2007 Serial 803
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Author Angel Sappa; M.A. Garcia
Title Coarse-to-Fine Approximation of Range Images with Bounded Error Adaptive Triangular Meshes Type Journal
Year 2007 Publication (up) Journal of Electronic Imaging, 16(2), 023010(11 pages) Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ SaG2007b Serial 802
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Author Xavier Otazu; Maria Vanrell
Title Perceptual representation of textured images Type Journal
Year 2005 Publication (up) Journal of Imaging Science and Technology, 49(3):262–271 (IF: 0.522) Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ OtV2005b Serial 542
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Author Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats
Title Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription Type Journal
Year 2017 Publication (up) Journal of Medical Engineering & Technology Abbreviated Journal JMET
Volume 41 Issue 6 Pages 486-497
Keywords
Abstract INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question.
OBJECTIVES:
To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination.
METHODS:
Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows.
RESULTS:
A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation).
CONCLUSIONS:
Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs.
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Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ EPB2017 Serial 3010
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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 (up) Journal of Medical Robotics Research Abbreviated Journal JMRR
Volume 3 Issue 2 Pages
Keywords convolutional neural networks; colonoscopy; computer aided diagnosis
Abstract 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 Agata Lapedriza; David Masip; Jordi Vitria
Title On the Use of External Face Features for Identity Verification Type Journal
Year 2006 Publication (up) Journal of Multimedia, 1(4): 11–20 Abbreviated Journal
Volume 1 Issue 4 Pages 11-20
Keywords Face Verification, Computer Vision, Machine Learning
Abstract In general automatic face classification applications images are captured in natural environments. In these cases, the performance is affected by variations in facial images related to illumination, pose, occlusion or expressions. Most of the existing face classification systems use only the internal features information, composed by eyes, nose and mouth, since they are more difficult to imitate. Nevertheless, nowadays a lot of applications not related to security are developed, and in these cases the information located at head, chin or ears zones (external features) can be useful to improve the current accuracies. However, the lack of a natural alignment in these areas makes difficult to extract these features applying classic Bottom-Up methods. In this paper, we propose a complete scheme based on a Top-Down reconstruction algorithm to extract external features of face images. To test our system we have performed face verification experiments using public databases, given that identity verification is a general task that has many real life applications. We have considered images uniformly illuminated, images with occlusions and images with high local changes in the illumination, and the obtained results show that the information contributed by the external features can be useful for verification purposes, specially significant when faces are partially occluded.
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ LMV2006b Serial 708
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Author Mariella Dimiccoli
Title Fundamentals of cone regression Type Journal
Year 2016 Publication (up) Journal of Statistics Surveys Abbreviated Journal
Volume 10 Issue Pages 53-99
Keywords cone regression; linear complementarity problems; proximal operators.
Abstract 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 Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda
Title PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities Type Journal
Year 2015 Publication (up) Journal of Technology and Science Education Abbreviated Journal JOTSE
Volume 5 Issue 2 Pages 87-96
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Notes IAM; ADAS; 600.076; 600.075 Approved no
Call Number Admin @ si @ MRG2015 Serial 2608
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; J.Roncaries; Debora Gil
Title Automatic evaluation of practices in Moodle for Self Learning in Engineering Type Journal
Year 2015 Publication (up) Journal of Technology and Science Education Abbreviated Journal JOTSE
Volume 5 Issue 2 Pages 97-106
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Notes IAM; DAG; 600.075; 600.077 Approved no
Call Number Admin @ si @ SRM2015 Serial 2610
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Author S. Tanimoto; N. Bruining; David Rotger; Petia Radeva; J. Ligthart; R.T. van Domburg; P. W. Serryus
Title Late Stent Recoil of the Bioabsorbable Everolimus Eluting Coronary Stent and its Relationship with Stent Struts Distribution and Plaque Morphology Type Journal
Year 2008 Publication (up) Journal of the American College of Cardiology, vol. 52(20):1616–1620 Abbreviated Journal
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Address Bridgewater, NJ 08807(USA)
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ TBR2008 Serial 953
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Author Robert Benavente; Maria Vanrell; Ramon Baldrich
Title Parametric Fuzzy Sets for Automatic Color Naming Type Journal
Year 2008 Publication (up) Journal of the Optical Society of America A Abbreviated Journal
Volume 25 Issue 10 Pages 2582–2593
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Notes CIC Approved no
Call Number CAT @ cat @ BVB2008 Serial 1004
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Author Matthias S. Keil; Gabriel Cristobal
Title Separating the chaff from the wheat: possible origins of the oblique effect Type Journal
Year 2000 Publication (up) Journal of the Optical Society of America A – Optics, Image Science, and Vision, 17(4): 697–710 (IF: 1.481) Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ KeC2000 Serial 630
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Author Josep Llados; Dimosthenis Karatzas; Joan Mas; Gemma Sanchez
Title A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives Type Journal
Year 2008 Publication (up) Journal of Universal Computer Science Abbreviated Journal
Volume 14 Issue 18 Pages 2912–2935
Keywords Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition
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Notes DAG Approved no
Call Number DAG @ dag @ LKM2008 Serial 1142
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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 Journal
Year 2019 Publication (up) 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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