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Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
Title | Subspace Procrustes Analysis | Type | Conference Article | ||
Year | 2014 | Publication ![]() |
ECCV Workshop on ChaLearn Looking at People | Abbreviated Journal | |
Volume | 8925 | Issue | Pages | 654-668 | |
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Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling dierent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benets of our approach. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | OR; HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ PTI2014 | Serial | 2539 | ||
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Author | Eloi Puertas; Miguel Angel Bautista; Daniel Sanchez; Sergio Escalera; Oriol Pujol | ||||
Title | Learning to Segment Humans by Stacking their Body Parts, | Type | Conference Article | ||
Year | 2014 | Publication ![]() |
ECCV Workshop on ChaLearn Looking at People | Abbreviated Journal | |
Volume | 8925 | Issue | Pages | 685-697 | |
Keywords | Human body segmentation; Stacked Sequential Learning | ||||
Abstract | Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body
part likelihood maps. These likelihood maps are obtained in a first stage by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline. |
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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 | ECCVW | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ PBS2014 | Serial | 2553 | ||
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Author | Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados | ||||
Title | Document Analysis Techniques for Automatic Electoral Document Processing: A Survey | Type | Conference Article | ||
Year | 2015 | Publication ![]() |
E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 139-141 | ||
Keywords | Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally | ||||
Abstract | In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. | ||||
Address | Bern; Switzerland; September 2015 | ||||
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 | VoteID | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ TCP2015 | Serial | 2641 | ||
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Author | Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner | ||||
Title | Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation | Type | Journal | ||
Year | 2011 | Publication ![]() |
e-Perimetron | Abbreviated Journal | ePER |
Volume | 6 | Issue | 4 | Pages | 219-229 |
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Abstract | By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput. | ||||
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Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ RRL2011a | Serial | 1765 | ||
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Author | David Roche; Debora Gil; Jesus Giraldo | ||||
Title | Multiple active receptor conformation, agonist efficacy and maximum effect of the system: the conformation-based operational model of agonism, | Type | Journal Article | ||
Year | 2013 | Publication ![]() |
Drug Discovery Today | Abbreviated Journal | DDT |
Volume | 18 | Issue | 7-8 | Pages | 365-371 |
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Abstract | The operational model of agonism assumes that the maximum effect a particular receptor system can achieve (the Em parameter) is fixed. Em estimates are above but close to the asymptotic maximum effects of endogenous agonists. The concept of Em is contradicted by superagonists and those positive allosteric modulators that significantly increase the maximum effect of endogenous agonists. An extension of the operational model is proposed that assumes that the Em parameter does not necessarily have a single value for a receptor system but has multiple values associated to multiple active receptor conformations. The model provides a mechanistic link between active receptor conformation and agonist efficacy, which can be useful for the analysis of agonist response under different receptor scenarios. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | IAM; 600.057; 600.054 | Approved | no | ||
Call Number | IAM @ iam @ RGG2013a | Serial | 2190 | ||
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Author | Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros | ||||
Title | From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example | Type | Book Chapter | ||
Year | 2017 | Publication ![]() |
Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
Volume | Issue | 13 | Pages | 243-258 | |
Keywords | Domain Adaptation | ||||
Abstract | Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. | ||||
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Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | ADAS; 600.085; 601.223; 600.076; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ LXG2017 | Serial | 2872 | ||
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Author | German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez | ||||
Title | Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA | Type | Book Chapter | ||
Year | 2017 | Publication ![]() |
Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
Volume | 12 | Issue | Pages | 227-241 | |
Keywords | SYNTHIA; Virtual worlds; Autonomous Driving | ||||
Abstract | Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. | ||||
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Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ RSV2017 | Serial | 2882 | ||
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Author | Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
Title | Fast Structural Matching for Document Image Retrieval through Spatial Databases | Type | Conference Article | ||
Year | 2014 | Publication ![]() |
Document Recognition and Retrieval XXI | Abbreviated Journal | |
Volume | 9021 | Issue | Pages | ||
Keywords | Document image retrieval; distance transform; MSER; spatial database | ||||
Abstract | The structure of document images plays a signicant role in document analysis thus considerable eorts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signicant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. | ||||
Address | Amsterdam; September 2014 | ||||
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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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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SPIE-DRR | ||
Notes | DAG; 600.056; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ GRK2014a | Serial | 2496 | ||
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Author | Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
Title | A Generic Image Retrieval Method for Date Estimation of Historical Document Collections | Type | Conference Article | ||
Year | 2022 | Publication ![]() |
Document Analysis Systems.15th IAPR International Workshop, (DAS2022) | Abbreviated Journal | |
Volume | 13237 | Issue | Pages | 583–597 | |
Keywords | Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG | ||||
Abstract | Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. | ||||
Address | La Rochelle, France; May 22–25, 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MGR2022 | Serial | 3694 | ||
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Author | Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A Multilingual Approach to Scene Text Visual Question Answering | Type | Conference Article | ||
Year | 2022 | Publication ![]() |
Document Analysis Systems.15th IAPR International Workshop, (DAS2022) | Abbreviated Journal | |
Volume | Issue | Pages | 65-79 | ||
Keywords | Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning | ||||
Abstract | Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines. | ||||
Address | La Rochelle, France; May 22–25, 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 611.004; 600.155; 601.002 | Approved | no | ||
Call Number | Admin @ si @ BGK2022b | Serial | 3695 | ||
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Author | 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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Publisher | Place of Publication | Editor | |||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ SVL2004 | Serial | 460 | ||
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Author | Ernest Valveny; Philippe Dosch | ||||
Title | Performance Evaluation of Symbol Recognition | Type | Book Chapter | ||
Year | 2004 | Publication ![]() |
Document Analysis Systems | Abbreviated Journal | LNCS |
Volume | 3163 | Issue | Pages | 354–365 | |
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Address | Springer-Verlag | ||||
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Publisher | Place of Publication | Editor | S. Marinai, A. Dengel (Eds.), | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 3-540-23060-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ VaD2004a | Serial | 502 | ||
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Author | Mickael Coustaty; Alicia Fornes | ||||
Title | Document Analysis and Recognition – ICDAR 2023 Workshops | Type | Book Whole | ||
Year | 2023 | Publication ![]() |
Document Analysis and Recognition – ICDAR 2023 Workshops | Abbreviated Journal | |
Volume | 14194 | Issue | 2 | Pages | |
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Address | San Jose; USA; August 2023 | ||||
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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 | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ CoF2023 | Serial | 3852 | ||
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Author | Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) | ||||
Title | 16th International Conference, 2021, Proceedings, Part III | Type | Book Whole | ||
Year | 2021 | Publication ![]() |
Document Analysis and Recognition – ICDAR 2021 | Abbreviated Journal | |
Volume | 12823 | Issue | Pages | ||
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Abstract | This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address | Lausanne, Switzerland, September 5-10, 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Cham | Place of Publication | Editor | Josep Llados; Daniel Lopresti; Seiichi Uchida | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-86333-3 | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3727 | ||
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Author | Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) | ||||
Title | 16th International Conference, 2021, Proceedings, Part IV | Type | Book Whole | ||
Year | 2021 | Publication ![]() |
Document Analysis and Recognition – ICDAR 2021 | Abbreviated Journal | |
Volume | 12824 | Issue | Pages | ||
Keywords | |||||
Abstract | This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address | Lausanne, Switzerland, September 5-10, 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Cham | Place of Publication | Editor | Josep Llados; Daniel Lopresti; Seiichi Uchida | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-86336-4 | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3728 | ||
Permanent link to this record |