| |
Records |
Links |
|
Author |
Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez |
|
| |
Title |
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
|
| |
Volume |
16 |
Issue |
1 |
Pages  |
50-66 |
|
| |
Keywords |
Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation |
|
| |
Abstract |
Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; ISE; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MEB2022 |
Serial |
3652 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
|
| |
Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
| |
Volume |
118 |
Issue |
1 |
Pages |
49–64 |
|
| |
Keywords |
Contextual rescoring; Poselets; Human pose estimation |
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer US |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0920-5691 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HES2016 |
Serial |
2719 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
|
| |
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” |
Type |
Journal |
| |
Year |
2018 |
Publication |
Informaciones Psiquiatricas |
Abbreviated Journal |
|
|
| |
Volume |
232 |
Issue |
|
Pages |
47-59 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0210-7279 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; no menciona |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FAA2018 |
Serial |
3214 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
|
| |
Title |
Deteccion automatica de la dominancia en conversaciones diadicas |
Type |
Journal Article |
| |
Year |
2010 |
Publication |
Escritos de Psicologia |
Abbreviated Journal |
EP |
|
| |
Volume |
3 |
Issue |
2 |
Pages |
41–45 |
|
| |
Keywords |
Dominance detection; Non-verbal communication; Visual features |
|
| |
Abstract |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1989-3809 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; OR; MILAB;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EMV2010 |
Serial |
1315 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
|
| |
Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
Journal of Signal Processing Systems |
Abbreviated Journal |
|
|
| |
Volume |
55 |
Issue |
1-3 |
Pages |
35–47 |
|
| |
Keywords |
|
|
| |
Abstract |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1939-8018 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HuPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EPM2009 |
Serial |
1258 |
|
| Permanent link to this record |
