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Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
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Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
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PRL |
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30 |
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15 |
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1424–1433 |
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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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BCNPCL @ bcnpcl @ EFP2009a |
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1180 |
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Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
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Title |
Deteccion automatica de la dominancia en conversaciones diadicas |
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Journal Article |
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2010 |
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Escritos de Psicologia |
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EP |
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3 |
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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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BCNPCL @ bcnpcl @ EMV2010 |
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1315 |
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Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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2009 |
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Journal of Signal Processing Systems |
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55 |
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1-3 |
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35–47 |
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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 radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. 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 sub-sets 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. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2009 |
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1258 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes |
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2007 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2007c |
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907 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Detection of Complex Salient Regions |
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2008 |
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EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2008b |
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960 |
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