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Author (down) Gemma Sanchez; Josep Llados
Title A Graph Grammar to Recognize Textured Symbols. Type Miscellaneous
Year 2001 Publication Sixth International Conference on Document Analysis and Recognition, ICDAR 2001, 465–469. Abbreviated Journal
Volume Issue Pages
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Address USA
Corporate Author Thesis
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Notes DAG Approved no
Call Number DAG @ dag @ SLl2001 Serial 162
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Author (down) Gemma Sanchez; Josep Llados
Title Syntactic models to represent perceptually regular repetitive patterns in graphic documents Type Miscellaneous
Year 2003 Publication Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 194–201 Abbreviated Journal
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Address Barcelona
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Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ SaL2003 Serial 417
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Author (down) Gemma Sanchez; Josep Llados
Title Syntactic models to represent perceptually regular repetitive patterns in graphic documents Type Miscellaneous
Year 2004 Publication Graphics Recognition: Recent Advances and Perspectives, Lecture Notes in Computer Science, J. Llados, Y.B. Kwon (Eds.), 3088:162–171 Abbreviated Journal
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Address Springer-Verlag
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Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ SaL2004 Serial 462
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Author (down) Gemma Sanchez; Ernest Valveny; Josep Llados; Joan Mas; N. Lozano
Title A platform to extract knowledge from graphic documents. Application to an architectural sketch understanding scenario Type Miscellaneous
Year 2004 Publication Document Analysis Systems VI, S. Marinai, A. Dengel (Eds.) Lecture Notes in Computer Science, 3163:389–400 Abbreviated Journal
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Address Springer-Verlag
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Notes DAG Approved no
Call Number DAG @ dag @ SVL2004 Serial 460
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Author (down) Gemma Sanchez; Ernest Valveny; Josep Llados; Enric Marti; Oriol Ramos Terrades; N.Lozano; Joan Mas
Title A system for virtual prototyping of architectural projects Type Conference Article
Year 2003 Publication Proceedings of Fifth IAPR International Workshop on Pattern Recognition Abbreviated Journal
Volume Issue Pages 65-74
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Address
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Notes DAG;IAM Approved no
Call Number IAM @ iam @ SVL2003 Serial 1650
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Author (down) Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados
Title Computer Vision Tools for Visually Impaired Children Learning Type Journal
Year 2007 Publication Abbreviated Journal
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Address
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Notes DAG Approved no
Call Number DAG @ dag @ SFM2007a Serial 891
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Author (down) Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados
Title Computer Vision Tools for Visually Impaired Children Learning Type Journal
Year 2007 Publication Abbreviated Journal
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ SFM2007b Serial 892
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Author (down) Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
Title Single view facial hair 3D reconstruction Type Conference Article
Year 2019 Publication 9th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 11867 Issue Pages 423-436
Keywords 3D Vision; Shape Reconstruction; Facial Hair Modeling
Abstract n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches.
Address Madrid; July 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IbPRIA
Notes ADAS; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ Serial 3707
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Author (down) Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
Title Detailed 3D face reconstruction from a single RGB image Type Journal
Year 2019 Publication Journal of WSCG Abbreviated Journal JWSCG
Volume 27 Issue 2 Pages 103-112
Keywords 3D Wrinkle Reconstruction; Face Analysis, Optimization.
Abstract This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
Address 2019/11
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes ADAS; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ Serial 3708
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Author (down) Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo
Title 2D-to-3D Facial Expression Transfer Type Conference Article
Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2008 - 2013
Keywords
Abstract Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular
mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic
equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.
Address
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Area Expedition Conference ICPR
Notes ADAS; 600.086; 600.130; 600.118 Approved no
Call Number Admin @ si @ RLM2018 Serial 3232
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Author (down) Gemma Rotger
Title Lifelike Humans: Detailed Reconstruction of Expressive Human Faces Type Book Whole
Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Felipe Lumbreras;Antonio Agudo
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-122714-3-0 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Rot2021 Serial 3513
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Author (down) Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool
Title Active MAP Inference in CRFs for Efficient Semantic Segmentation Type Conference Article
Year 2013 Publication 15th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 2312 - 2319
Keywords Semantic Segmentation
Abstract Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
Address Sydney; Australia; December 2013
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 1550-5499 ISBN Medium
Area Expedition Conference ICCV
Notes ADAS; 600.057 Approved no
Call Number ADAS @ adas @ RBN2013 Serial 2377
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Author (down) Gemma Roig; Xavier Boix; Fernando De la Torre
Title Optimal Feature Selection for Subspace Image Matching Type Conference Article
Year 2009 Publication 2nd IEEE International Workshop on Subspace Methods in conjunction Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features.
Address Kyoto, Japan
Corporate Author Thesis
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Language Summary Language Original Title
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Area Expedition Conference ICCV
Notes Approved no
Call Number Admin @ si @ RBT2009 Serial 1233
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Author (down) Gemma Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella
Title Hierarchical CRF with product label spaces for parts-based Models Type Conference Article
Year 2011 Publication IEEE Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages 657-664
Keywords Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features
Abstract Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset.
Address Santa Barbara, CA, USA, 2011
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference FG
Notes ADAS Approved no
Call Number Admin @ si @ RBT2011 Serial 1862
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Author (down) Galadrielle Humblot-Renaux; Sergio Escalera; Thomas B. Moeslund
Title Beyond AUROC & co. for evaluating out-of-distribution detection performance Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages 3880-3889
Keywords
Abstract While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric – Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc
Address Vancouver; Canada; June 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPRW
Notes HUPBA Approved no
Call Number Admin @ si @ HEM2023 Serial 3918
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