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Author Diego Velazquez; Pau Rodriguez; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez edit  url
openurl 
  Title A Closer Look at Embedding Propagation for Manifold Smoothing Type Journal Article
  Year 2022 Publication Journal of Machine Learning Research Abbreviated Journal JMLR  
  Volume 23 Issue 252 Pages 1-27  
  Keywords Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification  
  Abstract Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and
self-supervised learning performance.
 
  Address 9/2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) Approved no  
  Call Number Admin @ si @ VRG2022 Serial 3762  
Permanent link to this record
 

 
Author Jon Almazan; Bojana Gajic; Naila Murray; Diane Larlus edit  doi
openurl 
  Title Re-ID done right: towards good practices for person re-identification Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.  
  Address January 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) Approved no  
  Call Number Admin @ si @ Serial 3711  
Permanent link to this record
 

 
Author Hugo Bertiche; Meysam Madadi; Sergio Escalera edit  doi
openurl 
  Title Neural Cloth Simulation Type Journal Article
  Year 2022 Publication ACM Transactions on Graphics Abbreviated Journal ACMTGraph  
  Volume 41 Issue 6 Pages 1-14  
  Keywords  
  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–
 
  Address Dec 2022  
  Corporate Author Thesis  
  Publisher ACM Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) Approved no  
  Call Number Admin @ si @ BME2022b Serial 3779  
Permanent link to this record
 

 
Author Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal edit  url
openurl 
  Title SemiDocSeg: Harnessing Semi-Supervised Learning for Document Layout Analysis Type Miscellaneous
  Year 2024 Publication arXiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach.  
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  Notes (up) Approved no  
  Call Number Admin @ si @ BBL2024 Serial 4001  
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Author Yasuko Sugito; Javier Vazquez; Trevor Canham; Marcelo Bertalmio edit  doi
openurl 
  Title Image quality evaluation in professional HDR/WCG production questions the need for HDR metrics Type Journal Article
  Year 2022 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 31 Issue Pages 5163 - 5177  
  Keywords Measurement; Image color analysis; Image coding; Production; Dynamic range; Brightness; Extraterrestrial measurements  
  Abstract In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use.  
  Address  
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  Notes (up) 600.161; 611.007 Approved no  
  Call Number Admin @ si @ SVG2022 Serial 3683  
Permanent link to this record
 

 
Author Onur Ferhat; Fernando Vilariño; F. Javier Sanchez edit  url
openurl 
  Title A cheap portable eye-tracker solution for common setups. Type Journal Article
  Year 2014 Publication Journal of Eye Movement Research Abbreviated Journal JEMR  
  Volume 7 Issue 3 Pages 1-10  
  Keywords  
  Abstract We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system which can work on a computer with 30Hz sampling rate. After assessing the accuracy of our eye-tracker in elaborated experiments involving 12 subjects under 4 different system setups, we install it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.42° horizontal accuracy with 3Hz refresh rate for a building cost of 70 Euros.  
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  Notes (up) ;SIAI Approved no  
  Call Number Admin @ si @ FVS2014 Serial 2435  
Permanent link to this record
 

 
Author Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño edit   pdf
doi  openurl
  Title Growing Algorithm for Intersection Detection (GRAID) in branching patterns Type Journal Article
  Year 2015 Publication Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 26 Issue 2 Pages 387-400  
  Keywords Bifurcation ; Crossroad; Intersection ;Retina ; Vessel  
  Abstract Analysis of branching structures represents a very important task in fields such as medical diagnosis, road detection or biometrics. Detecting intersection landmarks Becomes crucial when capturing the structure of a branching pattern. We present a very simple geometrical model to describe intersections in branching structures based on two conditions: Bounded Tangency condition (BT) and Shortest Branch (SB) condition. The proposed model precisely sets a geometrical characterization of intersections and allows us to introduce a new unsupervised operator for intersection extraction. We propose an implementation that handles the consequences of digital domain operation that,unlike existing approaches, is not restricted to a particular scale and does not require the computation of the thinned pattern. The new proposal, as well as other existing approaches in the bibliography, are evaluated in a common framework for the first time. The performance analysis is based on two manually segmented image data sets: DRIVE retinal image database and COLON-VESSEL data set, a newly created data set of vascular content in colonoscopy frames. We have created an intersection landmark ground truth for each data set besides comparing our method in the only existing ground truth. Quantitative results confirm that we are able to outperform state-of-the-art performancelevels with the advantage that neither training nor parameter tuning is needed.  
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  Notes (up) ;SIAI Approved no  
  Call Number Admin @ si @MBS2015 Serial 2777  
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Author Fernando Vilariño; Dan Norton; Onur Ferhat edit  openurl
  Title Memory Fields: DJs in the Library Type Conference Article
  Year 2015 Publication 21 st Symposium of Electronic Arts Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Vancouver; Canada; August 2015  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ISEA  
  Notes (up) ;SIAI Approved no  
  Call Number Admin @ si @VNF2015 Serial 2800  
Permanent link to this record
 

 
Author A. Pujol; Felipe Lumbreras; X. Varona; Juan J. Villanueva edit  openurl
  Title Template matching through invariant eigenspace projection. Type Miscellaneous
  Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Bilbao  
  Corporate Author Thesis  
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  Area Expedition Conference  
  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ PLV1999 Serial 6  
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Author Antonio Lopez; D. Lloret; Joan Serrat edit   pdf
openurl 
  Title Creaseness measures for CT and MR image registration. Type Miscellaneous
  Year 1998 Publication CVPR’98 , IEEE Computer Society, pgs.694–699 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy  
  Address Santa Barbara, USA.  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
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  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ LLS1998a Serial 11  
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Author Antonio Lopez; Felipe Lumbreras; Joan Serrat edit  openurl
  Title Creaseness form level set extrinsec curvature. Type Miscellaneous
  Year 1998 Publication 5th European Conference on Computer Vision (ECCV’98), Lecture Notes in Computer Science,vol 1407, pgs. 156–169 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Freiburg, Germany.  
  Corporate Author Thesis  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ LLS1998b Serial 12  
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Author Antonio Lopez; Ricardo Toledo; Joan Serrat; Juan J. Villanueva edit  openurl
  Title Extraction of vessel centerlines from 2D coronary angiographies Type Miscellaneous
  Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. pgs. 489–496, volume I Abbreviated Journal  
  Volume Issue Pages  
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  Address Bilbao  
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  Area Expedition Conference  
  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ LTS1999 Serial 14  
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Author D. Lloret; Antonio Lopez; Joan Serrat edit  openurl
  Title 3-D image Processing and Modeling, workshop on non-linear model-based image analysis. Type Miscellaneous
  Year 1998 Publication NMBIA Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Glasgow, U.K.  
  Corporate Author Thesis  
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  Area Expedition Conference  
  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ LLS1998c Serial 15  
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Author Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever edit  openurl
  Title New improvements in the multiscale analysis of trabecular bone patterns. Type Miscellaneous
  Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes (SNRFAI’99), pags. 497–504 Abbreviated Journal  
  Volume Issue Pages  
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  Address Bilbao  
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  Area Expedition Conference  
  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ LNS1999 Serial 17  
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Author D. Lloret; Joan Serrat edit  openurl
  Title System for calibration of a stereotatic frame. Type Miscellaneous
  Year 1999 Publication Proceeding of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Bilbao  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) ADAS Approved no  
  Call Number ADAS @ adas @ LlS1999 Serial 20  
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