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Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera |
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Title |
Automatic Detection of Dominance and Expected Interest |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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12 |
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Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems. |
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1110-8657 |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ EPR2010d |
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1283 |
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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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Year |
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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Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
SRBF: Speckle Reducing Bilateral Filtering |
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2010 |
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Ultrasound in Medicine and Biology |
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UMB |
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36 |
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8 |
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1353-1363 |
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Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ BGP2010 |
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1314 |
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Author |
Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva |
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Title |
Fast Rigid Registration of Vascular Structures in IVUS Sequences |
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2009 |
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IEEE Transactions on Information Technology in Biomedicine |
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13 |
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6 |
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106-1011 |
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Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation. |
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1089-7771 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ GPL2009 |
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1250 |
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Author |
Oriol Pujol; David Masip |
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Title |
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary |
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Journal Article |
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Year |
2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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6 |
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1140–1146 |
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This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention |
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OR;HuPBA;MV;MILAB |
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BCNPCL @ bcnpcl @ PuM2009 |
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1252 |
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