| Records |
Author  |
Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol |
| Title |
La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals |
Type |
Journal |
| Year |
2016 |
Publication |
Lligall, Revista Catalana d'Arxivística |
Abbreviated Journal |
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| Volume |
39 |
Issue |
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Pages |
20-46 |
| Keywords |
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| Abstract |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG; 600.097 |
Approved |
no |
| Call Number |
Admin @ si @ FLR2016 |
Serial |
2897 |
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| Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny |
| Title |
Symbol Classification using Dynamic Aligned Shape Descriptor |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1957–1960 |
| Keywords |
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| Abstract |
Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. |
| Address |
Istanbul (Turkey) |
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Place of Publication |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG; HUPBA; MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ FEL2010 |
Serial |
1421 |
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| Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez |
| Title |
Symbol Recognition by Multi-class Blurred Shape Models |
Type |
Conference Article |
| Year |
2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
11–13 |
| Keywords |
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| Abstract |
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| Address |
Curitiba (Brazil) |
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Medium |
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| Area |
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Expedition |
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Conference |
GREC |
| Notes |
DAG; MILAB; HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ FEL2007b |
Serial |
910 |
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| Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez; Joan Mas |
| Title |
Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier |
Type |
Book Chapter |
| Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
Abbreviated Journal |
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| Volume |
5046 |
Issue |
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Pages |
30–40 |
| Keywords |
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| Abstract |
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Place of Publication |
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Editor |
W. Liu, J. Llados, J.M. Ogier |
| Language |
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LNCS |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG; HUPBA; MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ FEL2008 |
Serial |
989 |
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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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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
Girona (Spain) |
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Original Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;DAG;HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ FEL2007a |
Serial |
775 |
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| Author |
Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta |
| Title |
The ICDAR/GREC 2013 Music Scores Competition: Staff Removal |
Type |
Book Chapter |
| Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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| Volume |
8746 |
Issue |
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Pages |
207-220 |
| Keywords |
Competition; Graphics recognition; Music scores; Writer identification; Staff removal |
| Abstract |
The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
B.Lamiroy; J.-M. Ogier |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG; 600.077; 600.061 |
Approved |
no |
| Call Number |
Admin @ si @ FKV2014 |
Serial |
2581 |
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| Author |
Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados |
| Title |
ICDAR2017 Competition on Information Extraction in Historical Handwritten Records |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1389-1394 |
| Keywords |
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| Abstract |
The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results. |
| Address |
Kyoto; Japan; November 2017 |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 600.097; 601.225; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ FRB2017 |
Serial |
3052 |
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| Author |
Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke |
| Title |
A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors |
Type |
Conference Article |
| Year |
2011 |
Publication |
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
83-90 |
| Keywords |
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| Abstract |
The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
ACM |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-4503-0916-5 |
Medium |
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| Area |
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Expedition |
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Conference |
HIP |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FFF2011a |
Serial |
1823 |
| Permanent link to this record |
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| Author |
Alicia Fornes; Xavier Otazu; Josep Llados |
| Title |
Show through cancellation and image enhancement by multiresolution contrast processing |
Type |
Conference Article |
| Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
200-204 |
| Keywords |
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| Abstract |
Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities. |
| Address |
Washington; USA; August 2013 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1520-5363 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 602.006; 600.045; 600.061; 600.052;CIC |
Approved |
no |
| Call Number |
Admin @ si @ FOL2013 |
Serial |
2241 |
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| Author |
Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera |
| Title |
Towards Eating Habits Discovery in Egocentric Photo-Streams |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
9 |
Issue |
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Pages |
17495-17506 |
| Keywords |
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| Abstract |
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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Edition |
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Expedition |
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Conference |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MGR2021 |
Serial |
3637 |
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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 |
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| Volume |
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Issue |
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Pages |
718-728 |
| Keywords |
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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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Edition |
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Conference |
WACV |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ DBB2024 |
Serial |
3986 |
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| Author |
Alloy Das; Sanket Biswas; Umapada Pal; Josep Llados |
| Title |
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes |
Type |
Conference Article |
| Year |
2024 |
Publication |
IEEE International Conference on Robotics and Automation in PACIFICO |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance. |
| Address |
Yokohama; Japan; May 2024 |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
ICRA |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ DBP2024 |
Serial |
3979 |
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| Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
| Title |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
Type |
Journal Article |
| Year |
2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
| Volume |
28 |
Issue |
1 |
Pages |
87-101 |
| Keywords |
Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
| Abstract |
The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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Place of Publication |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
0921-7126 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
HUPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ CCE2015 |
Serial |
2549 |
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| Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
| Title |
Quantitative analysis of non-verbal communication for competence analysis |
Type |
Conference Article |
| Year |
2013 |
Publication |
16th Catalan Conference on Artificial Intelligence |
Abbreviated Journal |
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| Volume |
256 |
Issue |
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Pages |
105-114 |
| Keywords |
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| Abstract |
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| Address |
Vic; October 2013 |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
CCIA |
| Notes |
HUPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ CCE2013 |
Serial |
2324 |
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| Author |
Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
| Title |
Video Description Using Bidirectional Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
25th International Conference on Artificial Neural Networks |
Abbreviated Journal |
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| Volume |
2 |
Issue |
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Pages |
3-11 |
| Keywords |
Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
| Abstract |
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
| Address |
Barcelona; September 2016 |
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Summary Language |
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Original Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
ICANN |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ PBR2016 |
Serial |
2833 |
| Permanent link to this record |
