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Author |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
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Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
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Journal Article |
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Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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Volume |
118 |
Issue |
1 |
Pages |
49–64 |
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Keywords |
Contextual rescoring; Poselets; Human pose estimation |
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Abstract |
In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. |
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Springer US |
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0920-5691 |
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HuPBA;MILAB; |
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no |
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Admin @ si @ HES2016 |
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2719 |
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Author |
Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
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Title |
Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” |
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Journal |
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Year |
2018 |
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Informaciones Psiquiatricas |
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232 |
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47-59 |
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0210-7279 |
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HUPBA; no menciona |
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no |
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Admin @ si @ FAA2018 |
Serial |
3214 |
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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 |
Publication |
Escritos de Psicologia |
Abbreviated Journal |
EP |
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Volume |
3 |
Issue |
2 |
Pages |
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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BCNPCL @ bcnpcl @ EMV2010 |
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1315 |
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Author |
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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Journal Article |
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2009 |
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Journal of Signal Processing Systems |
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55 |
Issue |
1-3 |
Pages |
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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no |
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BCNPCL @ bcnpcl @ EPM2009 |
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1258 |
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Author |
E. Avotsa; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria |
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Title |
Automatic garment retexturing based on infrared information |
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Journal Article |
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Year |
2016 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
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Volume |
59 |
Issue |
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Pages |
28-38 |
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Keywords |
Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading |
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Abstract |
This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. |
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Elsevier |
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HuPBA;MILAB; |
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no |
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Call Number |
Admin @ si @ ADT2016 |
Serial |
2759 |
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Permanent link to this record |