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Author (up) Juan Andrade; T. Alejandra Vidal; A. Sanfeliu
Title Stochastic state estimation for simultaneous localization and map building in mobile robotics Type Book Chapter
Year 2005 Publication Vedran Kordic, Aleksandar Lazinica, and Munir Merdan (Eds.), Cutting Edge Robotics, Advanced Robotic Systems Press, 3.3:223–242 Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ AVS2005a Serial 565
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Author (up) Juan Andrade; T. Alejandra Vidal; A. Sanfeliu
Title Unscented transformation of vehicle states in SLAM Type Miscellaneous
Year 2005 Publication Proceedings of the IEEE International Conference on Robotics and Automation, 324–329 Abbreviated Journal
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Address Barcelona (Spain)
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Notes Approved no
Call Number Admin @ si @ AVS2005c Serial 591
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Author (up) Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil
Title BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation Type Journal Article
Year 2023 Publication Computer Methods and Programs in Biomedicine Abbreviated Journal CMPB
Volume 228 Issue Pages 107241
Keywords Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation
Abstract Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.
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Publisher Elsevier Place of Publication Editor
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Notes IAM; Approved no
Call Number Admin @ si @ BSC2023 Serial 3702
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Author (up) Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil
Title A benchmark for the evaluation of computational methods for bronchoscopic navigation Type Journal Article
Year 2022 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCARS
Volume 17 Issue 1 Pages
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Notes IAM Approved no
Call Number Admin @ si @ BSC2022 Serial 3832
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Author (up) Juan Diego Gomez
Title Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal
Volume 134 Issue Pages
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Corporate Author Computer Vision Center Thesis Master's thesis
Publisher Place of Publication Bellaterra, Barcelona Editor
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Notes MILAB Approved no
Call Number Admin @ si @ Gom2009 Serial 2393
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Author (up) Juan Ignacio Toledo
Title Information Extraction from Heterogeneous Handwritten Documents Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
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Abstract In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words.
In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
Address July 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Alicia Fornes;Josep Llados
Language Summary Language Original Title
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ISSN ISBN 978-84-948531-7-3 Medium
Area Expedition Conference
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ Tol2019 Serial 3389
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Author (up) Juan Ignacio Toledo; Alicia Fornes; Jordi Cucurull; Josep Llados
Title Election Tally Sheets Processing System Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 364-368
Keywords
Abstract In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost.
Address Santorini; Greece; April 2016
Corporate Author Thesis
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Area Expedition Conference DAS
Notes DAG; 602.006; 600.061; 601.225; 600.077; 600.097 Approved no
Call Number TFC2016 Serial 2752
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Author (up) 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
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
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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 (up) Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados
Title Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model Type Journal Article
Year 2019 Publication Pattern Recognition Abbreviated Journal PR
Volume 86 Issue Pages 27-36
Keywords Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks
Abstract Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches.
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Notes DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 Approved no
Call Number Admin @ si @ TCF2019 Serial 3166
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Author (up) Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados
Title Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling Type Conference Article
Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal
Volume 10029 Issue Pages 543-552
Keywords Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection
Abstract The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.
Address Merida; Mexico; December 2016
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-49054-0 Medium
Area Expedition Conference S+SSPR
Notes DAG; 600.097; 602.006 Approved no
Call Number Admin @ si @ TSF2016 Serial 2877
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Author (up) Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados
Title Handwriting Recognition by Attribute embedding and Recurrent Neural Networks Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1038-1043
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Abstract Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model
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Notes DAG; 600.097; 601.225; 600.121 Approved no
Call Number Admin @ si @ TDF2017 Serial 3055
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Author (up) Juan J. Villanueva
Title Visualization, Imaging and Image Processing. Type Book Whole
Year 2002 Publication International Association of Science and Technology for Development. ACTA Press, Abbreviated Journal
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ISSN ISBN 0–88986–354–3 Medium
Area Expedition Conference IASTE
Notes Approved no
Call Number ISE @ ise @ Vil2002 Serial 276
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Author (up) Juan J. Villanueva
Title Visualization, Imaging, and Image Processing, Type Book Whole
Year 2008 Publication Proceedings of the Eight IASTED International Conference Abbreviated Journal
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Address Palma de Mallorca (Spain)
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ISSN ISBN 978-0-88986-759-8 Medium
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Notes Approved no
Call Number ISE @ ise @ Vil2008 Serial 1003
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Author (up) Juan J. Villanueva; Jordi Gonzalez; Javier Varona; Xavier Roca
Title Aspaces: Action Spaces for Recognition and Synthesis of Human Actions. Type Miscellaneous
Year 2002 Publication II International Workshop Articulated Motion and Deformable Objects AMDO 2002. Abbreviated Journal
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Address Palma de Mallorca, Espanya
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Notes ISE Approved no
Call Number ISE @ ise @ VGV2002 Serial 302
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Author (up) Juan Jose Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger
Title Multi-class structural damage segmentation using fully convolutional networks Type Journal Article
Year 2019 Publication Computers in Industry Abbreviated Journal COMPUTIND
Volume 112 Issue Pages 103121
Keywords Bridge damage detection; Deep learning; Semantic segmentation
Abstract Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%.
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ RKL2019 Serial 3315
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