| |
Records |
Links |
|
Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
|
| |
Title |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
|
| |
Volume |
28 |
Issue  |
1 |
Pages |
87-101 |
|
| |
Keywords |
Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
|
| |
Abstract |
The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
|
| |
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 |
0921-7126 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CCE2015 |
Serial |
2549 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera |
|
| |
Title |
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transactions on System, Man and Cybernetics, Part B |
Abbreviated Journal |
TSMCB |
|
| |
Volume |
46 |
Issue |
1 |
Pages |
136-147 |
|
| |
Keywords |
Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data |
|
| |
Abstract |
We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. |
|
| |
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; MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BHE2016 |
Serial |
2566 |
|
| 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 |
Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
|
| |
Title |
Audio-Visual Emotion Recognition in Video Clips |
Type |
Journal Article |
| |
Year |
2019 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
|
| |
Volume |
10 |
Issue |
1 |
Pages |
60-75 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. |
|
| |
Address |
1 Jan.-March 2019 |
|
| |
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; 602.143; 602.133 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ NMN2017 |
Serial |
3011 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund |
|
| |
Title |
Organ Segmentation in Poultry Viscera Using RGB-D |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
18 |
Issue |
1 |
Pages |
117 |
|
| |
Keywords |
semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN |
|
| |
Abstract |
We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features. |
|
| |
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; no proj |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PVJ2018 |
Serial |
3072 |
|
| Permanent link to this record |
