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Author |
Fadi Dornaika; Bogdan Raducanu |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Person-specific face shape estimation under varying head pose from single snapshots |
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Conference Article |
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Year |
2010 |
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20th International Conference on Pattern Recognition |
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3496–3499 |
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This paper presents a new method for person-specific face shape estimation under varying head pose of a previously unseen person from a single image. We describe a featureless approach based on a deformable 3D model and a learned face subspace. The proposed approach is based on maximizing a likelihood measure associated with a learned face subspace, which is carried out by a stochastic and genetic optimizer. We conducted the experiments on a subset of Honda Video Database showing the feasibility and robustness of the proposed approach. For this reason, our approach could lend itself nicely to complex frameworks involving 3D face tracking and face gesture recognition in monocular videos. |
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Istanbul, Turkey |
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1051-4651 |
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978-1-4244-7542-1 |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2010b |
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1361 |
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Author |
Maya Dimitrova; Ch. Roumenin; Siya Lozanova; David Rotger; Petia Radeva |
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Title |
An Interface System Based on Multimodal Principle for Cardiological Diagnosis Assistance |
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Conference Article |
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Year |
2007 |
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International Conference On Computer Systems And Technologies |
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IIIB.4 |
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1–6 |
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Bulgaria |
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CompSysTech’07 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ DRL2007 |
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833 |
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Maya Dimitrova; Ch. Roumenin; Petia Radeva; David Rotger; Juan J. Villanueva |
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Title |
Multimodal Intelligent System for Cardiovascular Diagnosis |
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Miscellaneous |
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2003 |
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Automation and Informatics, any XXXVII, num. 3 |
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BCNPCL @ bcnpcl @ DRR2003 |
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374 |
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Maya Dimitrova; Petia Radeva; David Rotger; D. Boyadjiev; Juan J. Villanueva |
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Title |
Advanced Cardiological Diagnosis via Intelligent Image Analysis |
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Miscellaneous |
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2004 |
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Varna (Bulgaria) |
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MILAB |
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BCNPCL @ bcnpcl @ DRR2004 |
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477 |
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Author |
Maya Dimitrova; I. Terziev; Petia Radeva; Juan J. Villanueva |
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Title |
Java-Servlet Technology for Building New Web Document Classifiers |
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Miscellaneous |
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2004 |
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Varna (Bulgaria) |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ DTR2004 |
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476 |
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Author |
Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva |
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Title |
Text Detection in Urban Scenes (video sample) |
Type |
Conference Article |
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Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
202 |
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35–44 |
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Abstract. Text detection in urban scenes is a hard task due to the high variability of text appearance: different text fonts, changes in the point of view, or partial occlusion are just a few problems. Text detection can be specially suited for georeferencing business, navigation, tourist assistance, or to help visual impaired people. In this paper, we propose a general methodology to deal with the problem of text detection in outdoor scenes. The method is based on learning spatial information of gradient based features and Census Transform images using a cascade of classifiers. The method is applied in the context of Mobile Mapping systems, where a mobile vehicle captures urban image sequences. Moreover, a cover data set is presented and tested with the new methodology. The results show high accuracy when detecting multi-linear text regions with high variability of appearance, at same time that it preserves a low false alarm rate compared to classical approaches |
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Cardona (Spain) |
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978-1-60750-061-2 |
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CCIA |
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OR;MILAB;HuPBA;MV |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EBV2009 |
Serial |
1181 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Multi-class Binary Object Categorization using Blurred Shape Models |
Type |
Conference Article |
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Year |
2007 |
Publication |
Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern |
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4756 |
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773–782 |
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LCNS |
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978-3-540-76724-4 |
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CIARP |
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MILAB; DAG;HuPBA |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EFP2007 |
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911 |
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Permanent link to this record |
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Author |
Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
Type |
Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
30 |
Issue |
15 |
Pages |
1424–1433 |
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Abstract |
Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. |
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HuPBA; DAG; MILAB |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EFP2009a |
Serial |
1180 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva |
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Title |
Circular Blurred Shape Model for Symbol Spotting in Documents |
Type |
Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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Volume |
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Issue |
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Pages |
1985-1988 |
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Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. |
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Cairo, Egypt |
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978-1-4244-5653-6 |
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ICIP |
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MILAB;HuPBA;DAG |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EFP2009b |
Serial |
1184 |
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Permanent link to this record |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva |
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Title |
Multi-class Binary Symbol Classification with Circular Blurred Shape Models |
Type |
Conference Article |
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Year |
2009 |
Publication |
15th International Conference on Image Analysis and Processing |
Abbreviated Journal |
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Volume |
5716 |
Issue |
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Pages |
1005–1014 |
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Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. |
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Salerno, Italy |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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MILAB;HuPBA;DAG |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EFP2009c |
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1186 |
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Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
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Title |
Dominance Detection in Face-to-face Conversations |
Type |
Conference Article |
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2009 |
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2nd IEEE Workshop on CVPR for Human communicative Behavior analysis |
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97–102 |
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Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences. |
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Miami, USA |
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2160-7508 |
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978-1-4244-3994-2 |
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CVPR |
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HuPBA; OR; MILAB;MV |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EMV2009 |
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1227 |
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Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Deteccion automatica de la dominancia en conversaciones diadicas |
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Journal Article |
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Year |
2010 |
Publication |
Escritos de Psicologia |
Abbreviated Journal |
EP |
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Volume |
3 |
Issue |
2 |
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41–45 |
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Dominance detection; Non-verbal communication; Visual features |
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Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences. |
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1989-3809 |
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HUPBA; OR; MILAB;MV |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EMV2010 |
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1315 |
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Author |
Sergio Escalera; Oriol Pujol; Eric Laciar; Jordi Vitria; Esther Pueyo; Petia Radeva |
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Title |
Coronary Damage Classification of Patients with the Chagas Disease with Error-Correcting Output Codes |
Type |
Conference Article |
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Year |
2008 |
Publication |
Intelligent Systems, 4th International IEEE Conference, 6–8 setembre 2008. |
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2 |
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12–17 |
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The Chagaspsila disease is endemic in all Latin America, affecting millions of people in the continent. In order to diagnose and treat the Chagaspsila disease, it is important to detect and measure the coronary damage of the patient. In this paper, we analyze and categorize patients into different groups based on the coronary damage produced by the disease. Based on the features of the heart cycle extracted using high resolution ECG, a multi-class scheme of error-correcting output codes (ECOC) is formulated and successfully applied. The results show that the proposed scheme obtains significant performance improvements compared to previous works and state-of-the-art ECOC designs. |
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Varna (Bulgaria) |
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IS’08 |
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MILAB; OR;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ EPL2008 |
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1042 |
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Author |
Sergio Escalera; Oriol Pujol; Eric Laciar; Jordi Vitria; Esther Pueyo; Petia Radeva |
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Title |
Classification of Coronary Damage in Chronic Chagasic Patients |
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Book Chapter |
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2010 |
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Intelligent Systems – From Theory to Practice. Studies in Computational Intelligence |
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299 |
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461-478 |
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Chagas disease; Error-Correcting Output Codes; High resolution ECG; Decoding |
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Post Conference IEEE-IS 2008
The Chagas’ disease is endemic in all Latin America, affecting millions of people in the continent. In order to diagnose and treat the chagas’ disease, it is important to detect and measure the coronary damage of the patient. In this paper,
we analyze and categorize patients into different groups based on the coronary damage produced by the disease. Based on the features of the heart cycle extracted using high resolution ECG, a multi-class scheme of Error-Correcting Output Codes (ECOC)is formulated and successfully applied. The results show that the proposed scheme obtains significant performance improvements compared to previous works and state-of-the-art ECOC designs. |
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Springer-Verlag |
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V. Sgurev, M. Hadjiski (eds) |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ EPL2010 |
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1452 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
IVUS Tissue Characterization with Sub-class Error-correcting Output Codes |
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Conference Article |
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2008 |
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Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, pp. 1–8, 23–28 juny 2008. |
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Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different subsets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers and feature sets. |
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CVPR |
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MILAB;HuPBA |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
BCNPCL @ bcnpcl @ EPM2008 |
Serial |
1041 |
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