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| Author | Francisco Cruz; Oriol Ramos Terrades | ||||
Title  |
Handwritten Line Detection via an EM Algorithm | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 718-722 | ||
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| Abstract | In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results. | ||||
| Address | Washington; USA; August 2013 | ||||
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| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ CrT2013 | Serial | 2329 | ||
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| Author | Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez; Petia Radeva; Oriol Pujol | ||||
| Title |
Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction | Type | Book Chapter | ||
| Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:13–21 | Abbreviated Journal | |
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| Address | Girona (Spain) | ||||
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| Area | Expedition | Conference | |||
| Notes | MILAB;DAG;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ FEL2007a | Serial | 775 | ||
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| Author | 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 | Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title |
Handwritten Word Spotting by Inexact Matching of Grapheme Graphs | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 781 - 785 | ||
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| Abstract | This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077; 600.061; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ RLF2015b | Serial | 2642 | ||
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| Author | David Fernandez; Josep Llados; Alicia Fornes | ||||
| Title |
Handwritten Word Spotting in Old Manuscript Images Using a Pseudo-Structural Descriptor Organized in a Hash Structure | Type | Conference Article | ||
| Year | 2011 | Publication | 5th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 6669 | Issue | Pages | 628-635 | |
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| Abstract | There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this work, we have used a query-by-example formalism: we have matched an input image with one or multiple images from handwritten documents to determine the distance that might indicate a correspondence. We have developed an approach based in characteristic Loci Features stored in a hash structure. Document images of the marriage licences of the Cathedral of Barcelona are used as the benchmarking database. | ||||
| Address | Las Palmas de Gran Canaria. Spain | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | Jordi Vitria; Joao Miguel Raposo; Mario Hernandez | ||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | 978-3-642-21256-7 | Medium | ||
| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ FLF2011 | Serial | 1742 | ||
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| Author | David Fernandez | ||||
| Title |
Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 161 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Editor | |||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Fer2010b | Serial | 1353 | ||
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| Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title |
Handwritten Word Spotting with Corrected Attributes | Type | Conference Article | ||
| Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1017-1024 | ||
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| Abstract | We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | Medium | ||
| Area | Expedition | Conference | ICCV | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ AGF2013 | Serial | 2327 | ||
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| Author | Jose Antonio Rodriguez; Florent Perronnin | ||||
| Title |
Handwritten word-spotting using hidden Markov models and universal vocabularies | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 42 | Issue | 9 | Pages | 2103-2116 |
| Keywords | Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition | ||||
| Abstract | Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ RoP2009 | Serial | 1053 | ||
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| Author | Albert Andaluz | ||||
| Title |
Harmonic Phase Flow: User's guide | Type | Manual | ||
| Year | 2012 | Publication | CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | HPF is a plugin for the computation of clinical scores under Osirix.
This manual provides a basic guide for experienced clinical staff. Chapter 1 provides the theoretical background in which this plugin is based. Next, in chapter 2 we provide basic instructions for installing and uninstalling this plugin. chapter 3we shows a step-by-step scenario to compute clinical scores from tagged-MRI images with HPF. Finally, in chapter 4 we provide a quick guide for plugin developers |
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| Address | Bellaterra, Barcelona (Spain) | ||||
| Corporate Author | Computer Vision Center | Thesis | |||
| Publisher | CVC | Place of Publication | Barcelona | Editor | |
| Language | english | Summary Language | english | Original Title | |
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ And2012 | Serial | 1863 | ||
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| Author | Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez | ||||
| Title |
Harmony Potentials for Joint Classification and Segmentation | Type | Conference Article | ||
| Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3280–3287 | ||
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| Abstract | Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. | ||||
| Address | San Francisco CA, USA | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS;CIC;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ GBW2010 | Serial | 1296 | ||
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| Author | Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez | ||||
| Title |
Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 96 | Issue | 1 | Pages | 83-102 |
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| Abstract | The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi- nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE;CIC;ADAS | Approved | no | ||
| Call Number | Admin @ si @ BGW2012 | Serial | 1718 | ||
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| Author | Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya | ||||
| Title |
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance | Type | Conference Article | ||
| Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 718-728 | ||
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| Abstract | The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency. | ||||
| Address | Waikoloa; Hawai; USA; January 2024 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DBB2024 | Serial | 3986 | ||
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| Author | Franck Davoine; Fadi Dornaika | ||||
| Title |
Head and facial animation tracking using appearance-adaptive models and particle filters | Type | Book Chapter | ||
| Year | 2005 | Publication | V. Pavlovic and T.S. Huang (editors), Real–Time Vision for Human–Computer Interaction | Abbreviated Journal | |
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| Address | Springer-Verlag | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ DaD2005 | Serial | 599 | ||
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| Author | Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas | ||||
| Title |
Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices | Type | Journal Article | ||
| Year | 2016 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 175 | Issue | B | Pages | 866–876 |
| Keywords | Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices | ||||
| Abstract | During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. | ||||
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| Notes | LAMP; 600.072; 600.068; | Approved | no | ||
| Call Number | Admin @ si @ TRM2016 | Serial | 2721 | ||
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| Author | Ferran Poveda; Debora Gil; Enric Marti; Albert Andaluz; Manel Ballester;Francesc Carreras Costa | ||||
| Title |
Helical structure of the cardiac ventricular anatomy assessed by Diffusion Tensor Magnetic Resonance Imaging multi-resolution tractography | Type | Journal Article | ||
| Year | 2013 | Publication | Revista Española de Cardiología | Abbreviated Journal | REC |
| Volume | 66 | Issue | 10 | Pages | 782-790 |
| Keywords | Heart;Diffusion magnetic resonance imaging;Diffusion tractography;Helical heart;Myocardial ventricular band. | ||||
| Abstract | Deep understanding of myocardial structure linking morphology and function of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Several conceptual models of myocardial fiber organization have been proposed but the lack of an automatic and objective methodology prevented an agreement. We sought to deepen in this knowledge through advanced computer graphic representations of the myocardial fiber architecture by diffusion tensor magnetic resonance imaging (DT-MRI).
We performed automatic tractography reconstruction of unsegmented DT-MRI canine heart datasets coming from the public database of the Johns Hopkins University. Full scale tractographies have been build with 200 seeds and are composed by streamlines computed on the vectorial field of primary eigenvectors given at the diffusion tensor volumes. Also, we introduced a novel multi-scale visualization technique in order to obtain a simplified tractography. This methodology allowed to keep the main geometric features of the fiber tracts, making easier to decipher the main properties of the architectural organization of the heart. On the analysis of the output from our tractographic representations we found exact correlation with low-level details of myocardial architecture, but also with the more abstract conceptualization of a continuous helical ventricular myocardial fiber array. Objective analysis of myocardial architecture by an automated method, including the entire myocardium and using several 3D levels of complexity, reveals a continuous helical myocardial fiber arrangement of both right and left ventricles, supporting the anatomical model of the helical ventricular myocardial band described by Torrent-Guasp. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | IAM; 600.044; 600.060 | Approved | no | ||
| Call Number | IAM @ iam @ PGM2013 | Serial | 2194 | ||
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