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| Author | Hugo Bertiche; Meysam Madadi; Sergio Escalera | ||||
Title  |
PBNS: Physically Based Neural Simulation for Unsupervised Garment Pose Space Deformation | Type | Journal Article | ||
| Year | 2021 | Publication | ACM Transactions on Graphics | Abbreviated Journal | |
| Volume | 40 | Issue | 6 | Pages | 1-14 |
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| Abstract | We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to unsupervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth.
While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar. |
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ BME2021c | Serial | 3643 | ||
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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 | 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 | Enric Marti; Carme Julia; Debora Gil | ||||
| Title |
PBL en la docencia de gráficos por computador | Type | Miscellaneous | ||
| Year | 2007 | Publication | VII Jornadas de Aprendizaje Cooperativo (JAC07) | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 53-62 | |
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| Publisher | Place of Publication | Valladolid | Editor | ||
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| Notes | IAM;ADAS; | Approved | no | ||
| Call Number | IAM @ iam @ MJG2007b | Serial | 1604 | ||
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| Author | Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 129 | Issue | Pages | 108766 | |
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| Abstract | The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. | ||||
| Address | Sept. 2022 | ||||
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| Notes | DAG; 600.121; 600.162 | Approved | no | ||
| Call Number | Admin @ si @ KRR2022 | Serial | 3556 | ||
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| Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title |
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
| Volume | 22 | Issue | 2 | Pages | 502-514 |
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| Abstract | Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%. | ||||
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| Notes | ISE; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ RVC2020a | Serial | 3417 | ||
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| Author | F. Pla; Petia Radeva; Jordi Vitria | ||||
| Title |
Pattern Recognition: Progress, Directions and Applications | Type | Book Whole | ||
| Year | 2006 | Publication | Abbreviated Journal | ||
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| ISSN | ISBN | 84-933652-6-2 | Medium | ||
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| Notes | OR;MILAB;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ PRV2006b | Serial | 771 | ||
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| Author | V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich | ||||
| Title |
Pattern Recognition of Fragmented Objects with Adaptive Correlation Filters | Type | Miscellaneous | ||
| Year | 2006 | Publication | Topical Meeting on Optoinformatics / Information Photonics, 150–151 | Abbreviated Journal | |
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| Address | Saint-Petersburg (Russia) | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ KMA2006b | Serial | 674 | ||
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| Author | Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat | ||||
| Title |
Pattern Recognition and Image Analysis | Type | Book Whole | ||
| Year | 2007 | Publication | 3rd Iberian Conference | Abbreviated Journal | |
| Volume | 6669 | Issue | Pages | 4477-4478 | |
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| Address | Girona (Spain) | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ MBM2007 | Serial | 994 | ||
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| Author | Jordi Vitria; Joao Sanchez; Miguel Raposo; Mario Hernandez | ||||
| Title |
Pattern Recognition and Image Analysis | Type | Book Whole | ||
| Year | 2011 | Publication | 5th Iberian Conference Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 6669 | Issue | Pages | ||
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| Address | Las Palmas de Gran Canaria. Spain | ||||
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| Publisher | Springer-Verlag | Place of Publication | Berlin | Editor | J. Vitrià; J. Sanchez; M. Raposo; M. Hernandez |
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| ISSN | ISBN | 978-3-642-2125 | Medium | ||
| Area | Expedition | Conference | IbPRIA | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ VSR2011 | Serial | 1730 | ||
| Permanent link to this record | |||||
| Author | Jaume Garcia; Debora Gil; Francesc Carreras; Sandra Pujades; R.Leta; Xavier Alomar; Guillem Pons-LLados | ||||
| Title |
Patrons de Normalitat Regional per la Valoració de la Funció del Ventricle Esquerre | Type | Conference Article | ||
| Year | 2008 | Publication | XX Congrés de la Societat Catalana de Cardiologia | Abbreviated Journal | |
| Volume | Issue | Pages | 60 | ||
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| Abstract | Les malalties cardiovasculars afecten les propietats contràctils de la banda ventricular i provoquen una variació de la funció del Ventricle Esquerre (VE) . Només els indicadors locals (strains, la deformació del teixit) són capaços de detectar anomalies en territoris específics del VE . Patrons de normalitat regionals d’aquests paràmetres serien d’utilitat a l’hora de valorar-ne la funció .
Presentem un Domini Paramètric Normalitzat (DPN) que permet comparar dades de diferents pacients i definir Patrons de Normalitat Regional (PNR) |
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| Publisher | Place of Publication | Barcelona | Editor | ||
| Language | catalan | Summary Language | catalan | Original Title | |
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| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ GGC2008b | Serial | 1503 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Jaume Garcia; Mariano Vazquez; Ruth Aris; Guilleaume Houzeaux | ||||
| Title |
Patient-Sensitive Anatomic and Functional 3D Model of the Left Ventricle Function | Type | Conference Article | ||
| Year | 2008 | Publication | 8th World Congress on Computational Mechanichs (WCCM8) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Left Ventricle, Electromechanical Models, Image Processing, Magnetic Resonance. | ||||
| Abstract | Early diagnosis and accurate treatment of Left Ventricle (LV) dysfunction significantly increases the patient survival. Impairment of LV contractility due to cardiovascular diseases is reflected in its motion patterns. Recent advances in medical imaging, such as Magnetic Resonance (MR), have encouraged research on 3D simulation and modelling of the LV dynamics. Most of the existing 3D models [1] consider just the gross anatomy of the LV and restore a truncated ellipse which deforms along the cardiac cycle. The contraction mechanics of any muscle strongly depends on the spatial orientation of its muscular fibers since the motion that the muscle undergoes mainly takes place along the fibers. It follows that such simplified models do not allow evaluation of the heart electro-mechanical function and coupling, which has recently risen as the key point for understanding the LV functionality [2]. In order to thoroughly understand the LV mechanics it is necessary to consider the complete anatomy of the LV given by the orientation of the myocardial fibres in 3D space as described by Torrent Guasp [3].
We propose developing a 3D patient-sensitive model of the LV integrating, for the first time, the ven- tricular band anatomy (fibers orientation), the LV gross anatomy and its functionality. Such model will represent the LV function as a natural consequence of its own ventricular band anatomy. This might be decisive in restoring a proper LV contraction in patients undergoing pace marker treatment. The LV function is defined as soon as the propagation of the contractile electromechanical pulse has been modelled. In our experiments we have used the wave equation for the propagation of the electric pulse. The electromechanical wave moves on the myocardial surface and should have a conductivity tensor oriented along the muscular fibers. Thus, whatever mathematical model for electric pulse propa- gation [4] we consider, the complete anatomy of the LV should be extracted. The LV gross anatomy is obtained by processing multi slice MR images recorded for each patient. Information about the myocardial fibers distribution can only be extracted by Diffusion Tensor Imag- ing (DTI), which can not provide in vivo information for each patient. As a first approach, we have Figure 1: Scheme for the Left Ventricle Patient-Sensitive Model. computed an average model of fibers from several DTI studies of canine hearts. This rough anatomy is the input for our electro-mechanical propagation model simulating LV dynamics. The average fiber orientation is updated until the simulated LV motion agrees with the experimental evidence provided by the LV motion observed in tagged MR (TMR) sequences. Experimental LV motion is recovered by applying image processing, differential geometry and interpolation techniques to 2D TMR slices [5]. The pipeline in figure 1 outlines the interaction between simulations and experimental data leading to our patient-tailored model. |
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| Address | Venice; Italy | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 9788496736559 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ GGV2008b | Serial | 993 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Jaume Garcia; Manuel Vazquez; Ruth Aris; Guillaume Houzeaux | ||||
| Title |
Patient-Sensitive Anatomic and Functional 3D Model of the Left Ventricle Function | Type | Conference Article | ||
| Year | 2008 | Publication | 8th World Congress on Computational Mechanichs (WCCM8)/5th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Left Ventricle; Electromechanical Models; Image Processing; Magnetic Resonance. | ||||
| Abstract | Early diagnosis and accurate treatment of Left Ventricle (LV) dysfunction significantly increases the patient survival. Impairment of LV contractility due to cardiovascular diseases is reflected in its motion patterns. Recent advances in medical imaging, such as Magnetic Resonance (MR), have encouraged research on 3D simulation and modelling of the LV dynamics. Most of the existing 3D models consider just the gross anatomy of the LV and restore a truncated ellipse which deforms along the cardiac cycle. The contraction mechanics of any muscle strongly depends on the spatial orientation of its muscular fibers since the motion that the muscle undergoes mainly takes place along the fibers. It follows that such simplified models do not allow evaluation of the heart electro-mechanical function and coupling, which has recently risen as the key point for understanding the LV functionality . In order to thoroughly understand the LV mechanics it is necessary to consider the complete anatomy of the LV given by the orientation of the myocardial fibres in 3D space as described by Torrent Guasp. We propose developing a 3D patient-sensitive model of the LV integrating, for the first time, the ven- tricular band anatomy (fibers orientation), the LV gross anatomy and its functionality. Such model will represent the LV function as a natural consequence of its own ventricular band anatomy. This might be decisive in restoring a proper LV contraction in patients undergoing pace marker treatment. The LV function is defined as soon as the propagation of the contractile electromechanical pulse has been modelled. In our experiments we have used the wave equation for the propagation of the electric pulse. The electromechanical wave moves on the myocardial surface and should have a conductivity tensor oriented along the muscular fibers. Thus, whatever mathematical model for electric pulse propa- gation [4] we consider, the complete anatomy of the LV should be extracted. The LV gross anatomy is obtained by processing multi slice MR images recorded for each patient. Information about the myocardial fibers distribution can only be extracted by Diffusion Tensor Imag- ing (DTI), which can not provide in vivo information for each patient. As a first approach, we have computed an average model of fibers from several DTI studies of canine hearts. This rough anatomy is the input for our electro-mechanical propagation model simulating LV dynamics. The average fiber orientation is updated until the simulated LV motion agrees with the experimental evidence provided by the LV motion observed in tagged MR (TMR) sequences. Experimental LV motion is recovered by applying image processing, differential geometry and interpolation techniques to 2D TMR slices [5]. The pipeline in figure 1 outlines the interaction between simulations and experimental data leading to our patient-tailored model. | ||||
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| Publisher | Place of Publication | Venezia (Italia) | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | B-31470-08 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGV2008c | Serial | 1521 | ||
| Permanent link to this record | |||||
| Author | Fernando Vilariño; Petia Radeva | ||||
| Title |
Patch-Optimized Discriminant Active Contours for Medical Image Segmentation. | Type | Conference Article | ||
| Year | 2002 | Publication | Iberoamerican Conference on Artificial Intelligence | Abbreviated Journal | |
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| Address | Sevilla, Espanya | ||||
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| Publisher | Springer Verlag | Place of Publication | Editor | ||
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| Area | Expedition | Conference | IBERAMIA | ||
| Notes | MV;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ ViR2002; IAM @ iam @ VRa2003 | Serial | 320 | ||
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| Author | Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan | ||||
| Title |
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | 27 | Issue | Pages | 511-527 | |
| Keywords | particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging | ||||
| Abstract | In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor quality data, particles and trajectories can be characterized by an a-contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that do not require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well established baseline show that the proposed approach outperforms the state of the art. |
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ DJM2016 | Serial | 2735 | ||
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| Author | A. Quingles | ||||
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Particio de sòlids | Type | Report | ||
| Year | 2001 | Publication | CVC Technical Report #54 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Qui2001 | Serial | 206 | ||
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