| Records |
| Author |
Craig Von Land; Ricardo Toledo; Juan J. Villanueva |
| Title |
TeleRegions: Application of Telematics in Cardiac Care. |
Type |
Miscellaneous |
| Year |
1997 |
Publication  |
Computers in Cardiology, 1997. Piscataway, NJ: IEEE Computer Society Press, 24: 645–8. |
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ADAS |
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no |
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ISE @ ise @ VTV1997 |
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64 |
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| Author |
Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
| Title |
Automatic Digital Biometry Analysis based on Depth Maps |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
| Volume |
64 |
Issue |
9 |
Pages |
1316-1325 |
| Keywords |
Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
| Abstract |
World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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Elsevier |
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| Notes |
HuPBA;MILAB |
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no |
| Call Number |
Admin @ si @ RCR2013 |
Serial |
2252 |
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| Author |
Joan Serrat; Felipe Lumbreras; Antonio Lopez |
| Title |
Cost estimation of custom hoses from STL files and CAD drawings |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
| Volume |
64 |
Issue |
3 |
Pages |
299-309 |
| Keywords |
On-line quotation; STL format; Regression; Gaussian process |
| Abstract |
We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. |
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Elsevier |
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ADAS; 600.057; 600.054; 605.203 |
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no |
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Admin @ si @ SLL2013; ADAS @ adas @ |
Serial |
2161 |
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| Author |
Juan Jose Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
| Title |
Multi-class structural damage segmentation using fully convolutional networks |
Type |
Journal Article |
| Year |
2019 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
| Volume |
112 |
Issue |
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Pages |
103121 |
| Keywords |
Bridge damage detection; Deep learning; Semantic segmentation |
| Abstract |
Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%. |
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HuPBA; no proj |
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no |
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Admin @ si @ RKL2019 |
Serial |
3315 |
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| Author |
Chen Zhang; Maria del Mar Vila Muñoz; Petia Radeva; Roberto Elosua; Maria Grau; Angels Betriu; Elvira Fernandez-Giraldez; Laura Igual |
| Title |
Carotid Artery Segmentation in Ultrasound Images |
Type |
Conference Article |
| Year |
2015 |
Publication |
Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT2015), Joint MICCAI Workshops |
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Munich; Germany; October 2015 |
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CVII-STENT |
| Notes |
MILAB |
Approved |
no |
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Admin @ si @ ZVR2015 |
Serial |
2675 |
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| Author |
Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci |
| Title |
Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting |
Type |
Book Whole |
| Year |
2016 |
Publication |
Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting |
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Elsevier |
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ISBN |
9780128110188 |
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MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BZZ2016 |
Serial |
2821 |
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| Author |
Aura Hernandez-Sabate; Monica Mitiko; Sergio Shiguemi; Debora Gil |
| Title |
A validation protocol for assessing cardiac phase retrieval in IntraVascular UltraSound |
Type |
Conference Article |
| Year |
2010 |
Publication |
Computing in Cardiology |
Abbreviated Journal |
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| Volume |
37 |
Issue |
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Pages |
899-902 |
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| Abstract |
A good reliable approach to cardiac triggering is of utmost importance in obtaining accurate quantitative results of atherosclerotic plaque burden from the analysis of IntraVascular UltraSound. Although, in the last years, there has been an increase in research of methods for retrospective gating, there is no general consensus in a validation protocol. Many methods are based on quality assessment of longitudinal cuts appearance and those reporting quantitative numbers do not follow a standard protocol. Such heterogeneity in validation protocols makes faithful comparison across methods a difficult task. We propose a validation protocol based on the variability of the retrieved cardiac phase and explore the capability of several quality measures for quantifying such variability. An ideal detector, suitable for its application in clinical practice, should produce stable phases. That is, it should always sample the same cardiac cycle fraction. In this context, one should measure the variability (variance) of a candidate sampling with respect a ground truth (reference) sampling, since the variance would indicate how spread we are aiming a target. In order to quantify the deviation between the sampling and the ground truth, we have considered two quality scores reported in the literature: signed distance to the closest reference sample and distance to the right of each reference sample. We have also considered the residuals of the regression line of reference against candidate sampling. The performance of the measures has been explored on a set of synthetic samplings covering different cardiac cycle fractions and variabilities. From our simulations, we conclude that the metrics related to distances are sensitive to the shift considered while the residuals are robust against fraction and variabilities as far as one can establish a pair-wise correspondence between candidate and reference. We will further investigate the impact of false positive and negative detections in experimental data. |
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IEEE |
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0276-6547 |
ISBN |
978-1-4244-7318-2 |
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Conference |
CINC |
| Notes |
IAM; |
Approved |
no |
| Call Number |
IAM @ iam @ HSM2010 |
Serial |
1551 |
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| Author |
Marçal Rusiñol; Lluis Pere de las Heras; Joan Mas; Oriol Ramos Terrades; Dimosthenis Karatzas; Anjan Dutta; Gemma Sanchez; Josep Llados |
| Title |
CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012 |
Type |
Conference Article |
| Year |
2012 |
Publication |
Conference and Labs of the Evaluation Forum |
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Roma |
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CLEF |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ RHM2012 |
Serial |
2072 |
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| Author |
Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles |
| Title |
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
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Issue |
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Pages |
3467-3476 |
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| Abstract |
The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories. |
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Virtual; June 2021 |
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CVPRW |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MJE2021 |
Serial |
3560 |
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| Author |
Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak |
| Title |
DANICE: Domain adaptation without forgetting in neural image compression |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
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Issue |
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Pages |
1921-1925 |
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Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC. |
| Address |
Virtual; June 2021 |
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CVPRW |
| Notes |
LAMP; 600.120; 600.141; 601.379 |
Approved |
no |
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Admin @ si @ KHY2021 |
Serial |
3568 |
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| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Sabari Nathan; Priya Kansal; Armin Mehri; Parichehr Behjati Ardakani; A.Dalal; A.Akula; D.Sharma; S.Pandey; B.Kumar; J.Yao; R.Wu; KFeng; N.Li; Y.Zhao; H.Patel; V. Chudasama; K.Pjajapati; A.Sarvaiya; K.Upla; K.Raja; R.Ramachandra; C.Bush; F.Almasri; T.Vandamme; O.Debeir; N.Gutierrez; Q.Nguyen; W.Beksi |
| Title |
Thermal Image Super-Resolution Challenge – PBVS 2021 |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
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4359-4367 |
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This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results outperformed those from the first challenge, thus showing an improvement in both evaluation metrics. |
| Address |
Virtual; June 2021 |
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CVPRW |
| Notes |
MSIAU; 600.130; 600.122 |
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no |
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Admin @ si @ RSV2021 |
Serial |
3581 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Mohammad Sabokrou |
| Title |
Sign Language Production: A Review |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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Issue |
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Pages |
3472-3481 |
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| Abstract |
Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research. |
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Virtual; June 2021 |
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CVPRW |
| Notes |
HUPBA; no proj |
Approved |
no |
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Admin @ si @ RKE2021b |
Serial |
3603 |
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| Author |
Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez |
| Title |
Action-Based Representation Learning for Autonomous Driving |
Type |
Conference Article |
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2020 |
Publication |
Conference on Robot Learning |
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| Abstract |
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). |
| Address |
virtual; November 2020 |
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CORL |
| Notes |
ADAS; 600.118 |
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no |
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Admin @ si @ XCP2020 |
Serial |
3487 |
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| Author |
J. Mauri; Eduard Fernandez-Nofrerias; B. Garcia del Blanco; E. Iraculis; J.A. Gomez-Hospital; J. Comin; M.A. Sanchez Corral; F. Jara; A. Cequier; E. Esplugas; Debora Gil; J. Saludes; Petia Radeva; Ricardo Toledo; Juan J.Villanueva |
| Title |
Moviment del vas en l anàlisi d imatges d ecografia intracoronària: un model matemàtic |
Type |
Conference Article |
| Year |
2000 |
Publication |
Congrés de la Societat Catalana de Cardiologia. |
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IAM;RV;ISE;MILAB;ADAS |
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no |
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IAM @ iam @ MNG2000 |
Serial |
1621 |
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J. Mauri; Eduard Fernandez-Nofrerias; J. Comin; B. Garcia del Blanco; E. Iraculis; J.A. Gomez-Hospital; P. Valdovinos; F. Jara; A. Cequier; E. Esplugas; Oriol Pujol; Cristina Cañero; Debora Gil; Petia Radeva; Ricardo Toledo |
| Title |
Avaluació del Conjunt Stent/Artèria mitjançant ecografia intracoronària: lentorn informàtic |
Type |
Conference Article |
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2000 |
Publication |
Congrés de la Societat Catalana de Cardiologia. |
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Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
IAM;RV;MILAB;ADAS;HuPBA |
Approved |
no |
| Call Number |
IAM @ iam @ MNC2000 |
Serial |
1622 |
| Permanent link to this record |
