| Records |
| Author |
Julie Digne; Mariella Dimiccoli; Neus Sabater; Philippe Salembier |
Title  |
Neighborhood Filters and the Recovery of 3D Information |
Type |
Book Chapter |
| Year |
2015 |
Publication |
Handbook of Mathematical Methods in Imaging |
Abbreviated Journal |
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| Volume |
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Issue |
III |
Pages |
1645-1673 |
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| Abstract |
Following their success in image processing (see Chapter Local Smoothing Neighborhood Filters), neighborhood filters have been extended to 3D surface processing. This adaptation is not straightforward. It has led to several variants for surfaces depending on whether the surface is defined as a mesh, or as a raw data point set. The image gray level in the bilateral similarity measure is replaced by a geometric information such as the normal or the curvature. The first section of this chapter reviews the variants of 3D mesh bilateral filters and compares them to the simplest possible isotropic filter, the mean curvature motion.In a second part, this chapter reviews applications of the bilateral filter to a data composed of a sparse depth map (or of depth cues) and of the image on which they have been computed. Such sparse depth cues can be obtained by stereovision or by psychophysical techniques. The underlying assumption to these applications is that pixels with similar intensity around a region are likely to have similar depths. Therefore, when diffusing depth information with a bilateral filter based on locality and color similarity, the discontinuities in depth are assured to be consistent with the color discontinuities, which is generally a desirable property. In the reviewed applications, this ends up with the reconstruction of a dense perceptual depth map from the joint data of an image and of depth cues. |
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Springer New York |
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978-1-4939-0789-2 |
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MILAB |
Approved |
no |
| Call Number |
Admin @ si @ DDS2015 |
Serial |
2710 |
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| Author |
Hector Laria Mantecon; Kai Wang; Joost Van de Weijer; Bogdan Raducanu; Kai Wang |
| Title |
NeRF-Diffusion for 3D-Consistent Face Generation and Editing |
Type |
Conference Article |
| Year |
2024 |
Publication |
19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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Generating high-fidelity 3D-aware images without 3D supervision is a valuable capability in various applications. Current methods based on NeRF features, SDF information, or triplane features have limited variation after training. To address this, we propose a novel approach that combines pretrained models for shape and content generation. Our method leverages a pretrained Neural Radiance Field as a shape prior and a diffusion model for content generation. By conditioning the diffusion model with 3D features, we enhance its ability to generate novel views with 3D awareness. We introduce a consistency token shared between the NeRF module and the diffusion model to maintain 3D consistency during sampling. Moreover, our framework allows for text editing of 3D-aware image generation, enabling users to modify the style over 3D views while preserving semantic content. Our contributions include incorporating 3D awareness into a text-to-image model, addressing identity consistency in 3D view synthesis, and enabling text editing of 3D-aware image generation. We provide detailed explanations, including the shape prior based on the NeRF model and the content generation process using the diffusion model. We also discuss challenges such as shape consistency and sampling saturation. Experimental results demonstrate the effectiveness and visual quality of our approach. |
| Address |
Roma; Italia; February 2024 |
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VISAPP |
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LAMP |
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no |
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Admin @ si @ LWW2024 |
Serial |
4003 |
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| Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
| Title |
Neural Cloth Simulation |
Type |
Journal Article |
| Year |
2022 |
Publication |
ACM Transactions on Graphics |
Abbreviated Journal |
ACMTGraph |
| Volume |
41 |
Issue |
6 |
Pages |
1-14 |
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| Abstract |
We present a general framework for the garment animation problem through unsupervised deep learning inspired in physically based simulation. Existing trends in the literature already explore this possibility. Nonetheless, these approaches do not handle cloth dynamics. Here, we propose the first methodology able to learn realistic cloth dynamics unsupervisedly, and henceforth, a general formulation for neural cloth simulation. The key to achieve this is to adapt an existing optimization scheme for motion from simulation based methodologies to deep learning. Then, analyzing the nature of the problem, we devise an architecture able to automatically disentangle static and dynamic cloth subspaces by design. We will show how this improves model performance. Additionally, this opens the possibility of a novel motion augmentation technique that greatly improves generalization. Finally, we show it also allows to control the level of motion in the predictions. This is a useful, never seen before, tool for artists. We provide of detailed analysis of the problem to establish the bases of neural cloth simulation and guide future research into the specifics of this domain.
ACM Transactions on GraphicsVolume 41Issue 6December 2022 Article No.: 220pp 1– |
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Dec 2022 |
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ACM |
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no |
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Admin @ si @ BME2022b |
Serial |
3779 |
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| Author |
Manuel Carbonell |
| Title |
Neural Information Extraction from Semi-structured Documents A |
Type |
Book Whole |
| Year |
2020 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Issue |
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| Abstract |
Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part. |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Alicia Fornes;Mauricio Villegas;Josep Llados |
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978-84-122714-1-6 |
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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ Car20 |
Serial |
3483 |
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| Author |
Javier Varona; Juan J. Villanueva |
| Title |
Neural networks as spatial filters for image processing: Neurofilters |
Type |
Report |
| Year |
1996 |
Publication |
Technical Report #07 |
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CVC (UAB) |
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no |
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ISE @ ise @ VaV1996 |
Serial |
95 |
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| Author |
Javier Varona; Juan J. Villanueva |
| Title |
Neural Networks for Early Vision. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
Proceedings of the VII NSPRIA, Vol. I. |
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CVC (UAB) |
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ISE @ ise @ VaV1997b |
Serial |
62 |
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| Author |
Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu |
| Title |
NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results |
Type |
Conference Article |
| Year |
2022 |
Publication |
Understanding Social Behavior in Dyadic and Small Group Interactions |
Abbreviated Journal |
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| Volume |
191 |
Issue |
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Pages |
24-37 |
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We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. |
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PMLR |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ CCB2022 |
Serial |
3802 |
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| Author |
Javier Varona; Juan J. Villanueva |
| Title |
NeuroFilters: Neural Networks for image Processing. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
Vision Systems: New image Processing Techniques and Applications Algorithms, Methods, and Components. Proceedings of the SPIE. |
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Munich |
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no |
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ISE @ ise @ VaV1997a |
Serial |
207 |
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
| Title |
New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation |
Type |
Conference Article |
| Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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Issue |
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Pages |
265-269 |
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In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods. |
| Address |
Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ DTR2013b |
Serial |
2331 |
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| Author |
Fernando Vilariño; Enric Marti |
| Title |
New didactic techniques in the EHES applying mobile technologies |
Type |
Miscellaneous |
| Year |
2008 |
Publication |
Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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MILAB;IAM;MV;SIAI |
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no |
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IAM @ iam @ VIM2008 |
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1664 |
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| Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nikolay; B. Ter Haar Romeny; Juan J. Villanueva; M. Viergerver |
| Title |
New improvements in the multiscale analysis of trabecular bone patterns |
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Book Chapter |
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2000 |
Publication |
Pattern Recognition and Applications |
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251-260 |
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IOS Press |
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ADAS |
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no |
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Admin @ si @ |
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3418 |
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Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
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New improvements in the multiscale analysis of trabecular bone patterns. |
Type |
Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes (SNRFAI’99), pags. 497–504 |
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Bilbao |
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ADAS |
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no |
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ADAS @ adas @ LNS1999 |
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17 |
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Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
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New improvements in the multiscale analysis of trabecular bone patterns. |
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Miscellaneous |
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2000 |
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Pattern Recognition and Applications, IOS Press, 251–260. |
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ADAS |
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ADAS @ adas @ LNS2000 |
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332 |
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Carolina Malagelada; Fosca De Iorio; Fernando Azpiroz; Anna Accarino; Santiago Segui; Petia Radeva; Juan R. Malagelada |
| Title |
New Insight Into Intestinal Motor Function via Noninvasive Endoluminal Image Analysis |
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Journal |
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2008 |
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Gastroenterology |
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135 |
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4 |
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1155–1162 |
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MILAB |
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BCNPCL @ bcnpcl @ MDA2008 |
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1040 |
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Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
| Title |
New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired |
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Journal Article |
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2014 |
Publication |
Computer |
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COMP |
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47 |
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4 |
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52-58 |
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Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices. |
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0018-9162 |
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LAMP; |
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Admin @ si @ TSR2014a |
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2317 |
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