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F. Javier Sanchez; Jordi Vitria |
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Title  |
ViLi + : Extended Lisp for image Processing and Computer Vision. |
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1994 |
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Progress in Image Analysis and Processing III |
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World Scientific |
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S.Impedovo |
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981-02-1552-5 |
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MV;OR |
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BCNPCL @ bcnpcl @ SaV1994; IAM @ iam @ SaV1994 |
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114 |
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Bhaskar Chakraborty; Ognjen Rudovic; Jordi Gonzalez |
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View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts |
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2008 |
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8th IEEE International Conference on Automatic Face and Gesture Recognition |
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Amsterdam; The Netherlands |
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ISE @ ise @ CRG2008 |
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1113 |
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Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera |
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Title |
Video-based Skill Assessment for Golf: Estimating Golf Handicap |
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Conference Article |
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2023 |
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Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
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31-39 |
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Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. |
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Otawa; Canada; October 2023 |
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MMSports |
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HUPBA |
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Admin @ si @ KXC2023 |
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3929 |
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Marc Bolaños; Maite Garolera; Petia Radeva |
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Video Segmentation of Life-Logging Videos |
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2014 |
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8th Conference on Articulated Motion and Deformable Objects |
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8563 |
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1-9 |
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AMDO |
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MILAB |
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Admin @ si @ BGR2014 |
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2558 |
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Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
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Title |
Video Description Using Bidirectional Recurrent Neural Networks |
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2016 |
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25th International Conference on Artificial Neural Networks |
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2 |
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3-11 |
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Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
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Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
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Barcelona; September 2016 |
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ICANN |
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MILAB; |
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no |
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Admin @ si @ PBR2016 |
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2833 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Video Co-segmentation |
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Conference Article |
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Year |
2012 |
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11th Asian Conference on Computer Vision |
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7725 |
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13-24 |
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Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos. |
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Daejeon, Korea |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-37443-2 |
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ACCV |
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ADAS |
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no |
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Admin @ si @ RSL2012d |
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2153 |
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Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva |
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Title |
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering |
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Conference Article |
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2017 |
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8th Iberian Conference on Pattern Recognition and Image Analysis |
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Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks |
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In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. |
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Faro; Portugal; June 2017 |
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IbPRIA |
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MILAB; no proj |
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Admin @ si @ BPC2017 |
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2939 |
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Author |
Aura Hernandez-Sabate; Petia Radeva; Antonio Tovar; Debora Gil |
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Title |
Vessel structures alignment by spectral analysis of ivus sequences |
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Conference Article |
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2006 |
Publication |
Proc. of CVII, MICCAI Workshop |
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39-36 |
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Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS. |
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Copenhaguen (Denmark), |
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1st International Wokshop on Computer Vision for Intravascular and Intracardiac Imaging (CVII’06) |
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IAM; MILAB |
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IAM @ iam @ HRT2006 |
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1552 |
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Author |
Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados |
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Vers une approche foue of encapsulation de graphes: application a la reconnaissance de symboles |
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Conference Article |
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2010 |
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Colloque International Francophone sur l'Écrit et le Document |
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169-184 |
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Fuzzy interval; Graph embedding; Bayesian network; Symbol recognition |
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We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented. |
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Sousse, Tunisia |
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CIFED |
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DAG |
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DAG @ dag @ LBR2010a |
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1293 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Vehicle Trajectory Estimation based on Monocular Vision |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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587-594 |
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vehicle detection |
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Girona (Spain) |
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ADAS @ adas @ PoL2007a |
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785 |
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Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez |
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Vehicle geolocalization based on video synchronization |
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Conference Article |
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2010 |
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13th Annual International Conference on Intelligent Transportation Systems |
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1511–1516 |
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video alignment |
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TC8.6
This paper proposes a novel method for estimating the geospatial localization of a vehicle. I uses as input a georeferenced video sequence recorded by a forward-facing camera attached to the windscreen. The core of the proposed method is an on-line video synchronization which finds out the corresponding frame in the georeferenced video sequence to the one recorded at each time by the camera on a second drive through the same track. Once found the corresponding frame in the georeferenced video sequence, we transfer its geospatial information of this frame. The key advantages of this method are: 1) the increase of the update rate and the geospatial accuracy with regard to a standard low-cost GPS and 2) the ability to localize a vehicle even when a GPS is not available or is not reliable enough, like in certain urban areas. Experimental results for an urban environments are presented, showing an average of relative accuracy of 1.5 meters. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ITSC |
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ADAS |
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ADAS @ adas @ DPS2010 |
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1423 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Vegetation Index Estimation from Monospectral Images |
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Conference Article |
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2018 |
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15th International Conference on Images Analysis and Recognition |
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10882 |
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353-362 |
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This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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Povoa de Varzim; Portugal; June 2018 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018c |
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3196 |
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Ferran Poveda; Jaume Garcia; Enric Marti; Debora Gil |
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Validation of the myocardial architecture in DT-MRI tractography |
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2010 |
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Medical Image Computing in Catalunya: Graduate Student Workshop |
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29-30 |
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Deep understanding of myocardial structure may help to link form and funcion of the heart unraveling crucial knowledge for medical and surgical clinical procedures and studies. In this work we introduce two visualization techniques based on DT-MRI streamlining able to decipher interesting properties of the architectural organization of the heart. |
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Girona (Spain) |
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MICCAT |
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IAM |
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IAM @ iam @ PGM2010 |
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1626 |
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Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera |
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Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal |
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Conference Article |
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2016 |
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4th Congreso WCPT-SAR |
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Buenos Aires; Argentina; June 2016 |
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WCPT-SAR |
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HuPBA;MILAB |
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Admin @ si @ RRR2016 |
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2853 |
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Author |
Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto |
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Va-ID: A trust-based virtual assessment system |
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Conference Article |
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2014 |
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6th International Conference on Intelligent Networking and Collaborative Systems |
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328 - 335 |
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Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model. |
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Salerna; Italy; September 2014 |
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978-1-4799-6386-7 |
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INCOS |
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OR; HuPBA;MV |
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no |
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Admin @ si @ ABB2014 |
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2620 |
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