Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.; Guimond, S.
Prediction of Emotional States from Partial Facial Features for Virtual Reality Applications Article de journal
Dans: Annual Review of CyberTherapy and Telemedicine, vol. 21, p. 17–21, 2023, ISSN: 15548716, (Publisher: Interactive Media Institute).
Résumé | Liens | BibTeX | Étiquettes: Arousal, article, clinical article, convolutional neural network, correlation coefficient, data base, emotion, facies, female, human, human experiment, Image processing, long short term memory network, male, random forest, residual neural network, root mean squared error, videorecording, virtual reality
@article{joudeh_prediction_2023-1,
title = {Prediction of Emotional States from Partial Facial Features for Virtual Reality Applications},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard and S. Guimond},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182471413&partnerID=40&md5=8190e0dbb5b48ae508515f4029b0a0d1},
issn = {15548716},
year = {2023},
date = {2023-01-01},
journal = {Annual Review of CyberTherapy and Telemedicine},
volume = {21},
pages = {17–21},
abstract = {The availability of virtual reality (VR) in numerous clinical contexts has been made possible by recent technological advancements. One application is using VR for cognitive interventions with individuals who have mental disorders. Predicting the emotional states of users could help to prevent their discouragement during VR interventions. We can monitor the emotional states of individuals using sensors like an external camera, as they engage in various tasks within VR environments. The emotional state of VR users can be measured through arousal and valence, as per the Circumplex model. We used the Remote Collaborative and Affective Interactions (RECOLA) database of emotional behaviours. We processed video frames from 18 RECOLA videos. Due to the headset in VR systems, we detected faces and cropped the images of faces to use the lower half of the face only. We labeled the images with arousal and valence values to reflect various emotions. Convolutional neural networks (CNNs), specifically MobileNet-v2 and ResNets-18, were then used to predict arousal and valence values. MobileNet-v2 outperforms ResNet-18 as well as others from the literature. We achieved a root mean squared error (RMSE), Pearson’s correlation coefficient (PCC), and Concordance correlation coefficient (CCC) of 0.1495, 0.6387, and 0.6081 for arousal, and 0.0996, 0.6453, and 0.6232 for valence. Our work acts as a proof-of-concept for predicting emotional states from arousal and valence values via visual data of users immersed in VR experiences. In the future, predicted emotions could be used to automatically adjust the VR environment for individuals engaged in cognitive interventions. © 2023, Interactive Media Institute. All rights reserved.},
note = {Publisher: Interactive Media Institute},
keywords = {Arousal, article, clinical article, convolutional neural network, correlation coefficient, data base, emotion, facies, female, human, human experiment, Image processing, long short term memory network, male, random forest, residual neural network, root mean squared error, videorecording, virtual reality},
pubstate = {published},
tppubtype = {article}
}
The availability of virtual reality (VR) in numerous clinical contexts has been made possible by recent technological advancements. One application is using VR for cognitive interventions with individuals who have mental disorders. Predicting the emotional states of users could help to prevent their discouragement during VR interventions. We can monitor the emotional states of individuals using sensors like an external camera, as they engage in various tasks within VR environments. The emotional state of VR users can be measured through arousal and valence, as per the Circumplex model. We used the Remote Collaborative and Affective Interactions (RECOLA) database of emotional behaviours. We processed video frames from 18 RECOLA videos. Due to the headset in VR systems, we detected faces and cropped the images of faces to use the lower half of the face only. We labeled the images with arousal and valence values to reflect various emotions. Convolutional neural networks (CNNs), specifically MobileNet-v2 and ResNets-18, were then used to predict arousal and valence values. MobileNet-v2 outperforms ResNet-18 as well as others from the literature. We achieved a root mean squared error (RMSE), Pearson’s correlation coefficient (PCC), and Concordance correlation coefficient (CCC) of 0.1495, 0.6387, and 0.6081 for arousal, and 0.0996, 0.6453, and 0.6232 for valence. Our work acts as a proof-of-concept for predicting emotional states from arousal and valence values via visual data of users immersed in VR experiences. In the future, predicted emotions could be used to automatically adjust the VR environment for individuals engaged in cognitive interventions. © 2023, Interactive Media Institute. All rights reserved.
2.
Allili, M. S.; Ziou, D.
Likelihood-based feature relevance for figure-ground segmentation in images and videos Article de journal
Dans: Neurocomputing, vol. 167, p. 658–670, 2015, ISSN: 09252312, (Publisher: Elsevier).
Résumé | Liens | BibTeX | Étiquettes: accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording
@article{allili_likelihood-based_2015,
title = {Likelihood-based feature relevance for figure-ground segmentation in images and videos},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952631642&doi=10.1016%2fj.neucom.2015.04.015&partnerID=40&md5=833948d0784e0dc42c2245b9343971dd},
doi = {10.1016/j.neucom.2015.04.015},
issn = {09252312},
year = {2015},
date = {2015-01-01},
journal = {Neurocomputing},
volume = {167},
pages = {658–670},
abstract = {We propose an efficient method for image/video figure-ground segmentation using feature relevance (FR) and active contours. Given a set of positive and negative examples of a specific foreground (an object of interest (OOI) in an image or a tracked objet in a video), we first learn the foreground distribution model and its characteristic features that best discriminate it from its contextual background. For this goal, an objective function based on feature likelihood ratio is proposed for supervised FR computation. FR is then incorporated in foreground segmentation of new images and videos using level sets and energy minimization. We show the effectiveness of our approach on several examples of image/video figure-ground segmentation. © 2015 Elsevier B.V.},
note = {Publisher: Elsevier},
keywords = {accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording},
pubstate = {published},
tppubtype = {article}
}
We propose an efficient method for image/video figure-ground segmentation using feature relevance (FR) and active contours. Given a set of positive and negative examples of a specific foreground (an object of interest (OOI) in an image or a tracked objet in a video), we first learn the foreground distribution model and its characteristic features that best discriminate it from its contextual background. For this goal, an objective function based on feature likelihood ratio is proposed for supervised FR computation. FR is then incorporated in foreground segmentation of new images and videos using level sets and energy minimization. We show the effectiveness of our approach on several examples of image/video figure-ground segmentation. © 2015 Elsevier B.V.
3.
Stetz, M. C.; Kaloi-Chen, J. Y.; Turner, D. D.; Bouchard, S.; Riva, G.; Wiederhold, B. K.
The effectiveness of Technology-Enhanced relaxation techniques for military medical warriors Article de journal
Dans: Military Medicine, vol. 176, no 9, p. 1065–1070, 2011, ISSN: 00264075, (Publisher: Association of Military Surgeons of the US).
Résumé | Liens | BibTeX | Étiquettes: adult, Anxiety, article, clinical trial, computer interface, controlled clinical trial, controlled study, female, human, Humans, male, mental stress, methodology, Military Personnel, Psychological, psychological aspect, questionnaire, Questionnaires, randomized controlled trial, Relaxation Therapy, relaxation training, soldier, Stress, User-Computer Interface, Video recording, videorecording
@article{stetz_effectiveness_2011,
title = {The effectiveness of Technology-Enhanced relaxation techniques for military medical warriors},
author = {M. C. Stetz and J. Y. Kaloi-Chen and D. D. Turner and S. Bouchard and G. Riva and B. K. Wiederhold},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052455147&doi=10.7205%2fMILMED-D-10-00393&partnerID=40&md5=dce993c0b65bb351edd74816a0d65450},
doi = {10.7205/MILMED-D-10-00393},
issn = {00264075},
year = {2011},
date = {2011-01-01},
journal = {Military Medicine},
volume = {176},
number = {9},
pages = {1065–1070},
abstract = {Combat zones can be very stressful for those in the area. Even in the battlefi eld, military medical personnel are expected to save others, while also staying alive. In this study, half of a sample of deployed military medical warriors (total n = 60) participated in technology-assisted relaxation training. Learning relaxation skills with a video clip of virtual reality relaxing scenes showed a statistically signifi cant impact on the anxiety levels of the Experimental Group. © Association of Military Surgeons of the U.S. All rights reserved.},
note = {Publisher: Association of Military Surgeons of the US},
keywords = {adult, Anxiety, article, clinical trial, computer interface, controlled clinical trial, controlled study, female, human, Humans, male, mental stress, methodology, Military Personnel, Psychological, psychological aspect, questionnaire, Questionnaires, randomized controlled trial, Relaxation Therapy, relaxation training, soldier, Stress, User-Computer Interface, Video recording, videorecording},
pubstate = {published},
tppubtype = {article}
}
Combat zones can be very stressful for those in the area. Even in the battlefi eld, military medical personnel are expected to save others, while also staying alive. In this study, half of a sample of deployed military medical warriors (total n = 60) participated in technology-assisted relaxation training. Learning relaxation skills with a video clip of virtual reality relaxing scenes showed a statistically signifi cant impact on the anxiety levels of the Experimental Group. © Association of Military Surgeons of the U.S. All rights reserved.
4.
Allili, M. S.; Ziou, D.
Object tracking in videos using adaptive mixture models and active contours Article de journal
Dans: Neurocomputing, vol. 71, no 10-12, p. 2001–2011, 2008, ISSN: 09252312.
Résumé | Liens | BibTeX | Étiquettes: Active contours, algorithm, Algorithms, article, controlled study, Image analysis, Image processing, imaging system, Level set method, Mathematical models, motion analysis system, Object recognition, priority journal, Set theory, statistical model, Video cameras, Video sequences, videorecording, visual information
@article{allili_object_2008,
title = {Object tracking in videos using adaptive mixture models and active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-44649197137&doi=10.1016%2fj.neucom.2007.10.019&partnerID=40&md5=a2aef677fae1b220f68c9fd720be3fd5},
doi = {10.1016/j.neucom.2007.10.019},
issn = {09252312},
year = {2008},
date = {2008-01-01},
journal = {Neurocomputing},
volume = {71},
number = {10-12},
pages = {2001–2011},
abstract = {In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods. © 2008 Elsevier B.V. All rights reserved.},
keywords = {Active contours, algorithm, Algorithms, article, controlled study, Image analysis, Image processing, imaging system, Level set method, Mathematical models, motion analysis system, Object recognition, priority journal, Set theory, statistical model, Video cameras, Video sequences, videorecording, visual information},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods. © 2008 Elsevier B.V. All rights reserved.
5.
Bouchard, S.; Paquin, B.; Payeur, R.; Allard, M.; Rivard, V.; Fournier, T.; Renaud, P.; Lapierre, J.
Delivering Cognitive-Behavior Therapy for Panic Disorder with Agoraphobia in Videoconference Article de journal
Dans: Telemedicine and e-Health, vol. 10, no 1, p. 13–25, 2004, ISSN: 15305627 (ISSN), (Publisher: Mary Ann Liebert Inc.).
Résumé | Liens | BibTeX | Étiquettes: adult, agoraphobia, article, behavior therapy, clinical article, Cognitive systems, Cognitive-behavior therapy (CBT), female, Health care, health care delivery, human, male, Medical problems, panic, Patient monitoring, priority journal, psychotherapy, telecommunication, Telemedicine, Therapeutic alliances, validation process, Video conferencing, videorecording
@article{bouchard_delivering_2004,
title = {Delivering Cognitive-Behavior Therapy for Panic Disorder with Agoraphobia in Videoconference},
author = {S. Bouchard and B. Paquin and R. Payeur and M. Allard and V. Rivard and T. Fournier and P. Renaud and J. Lapierre},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-1842783565&doi=10.1089%2f153056204773644535&partnerID=40&md5=b16d49e06152cafecd086aa08c3326b7},
doi = {10.1089/153056204773644535},
issn = {15305627 (ISSN)},
year = {2004},
date = {2004-01-01},
journal = {Telemedicine and e-Health},
volume = {10},
number = {1},
pages = {13–25},
abstract = {Delivering psychotherapy by videoconference could significantly increase the accessibility of empirically validated treatments. The aim of this study was to compare the effectiveness of cognitive-behavior therapy (CBT) for panic disorder with agoraphobia (PDA) when the therapy is delivered either face-to-face or by videoconference. A sample of 21 participants was treated either face-to-face or by videoconference. Results showed that CBT delivered by videoconference was as effective as CBT delivered face-to-face. There was a statistically significant reduction in all measures, and the number of panic-free participants among those receiving CBT by videoconference was 81% at post-treatment and 91% at the 6-month follow-up. None of the comparisons with face-to-face psychotherapy suggested that CBT delivered by videoconference was less effective. These results were confirmed by analyses of effect size. The participants reported the development of an excellent therapeutic alliance in videoconference as early as the first therapy session. The importance of these results for treatment accessibility is discussed. Hypotheses are proposed to explain the rapid creation of strong therapeutic alliances in videoconferencing.},
note = {Publisher: Mary Ann Liebert Inc.},
keywords = {adult, agoraphobia, article, behavior therapy, clinical article, Cognitive systems, Cognitive-behavior therapy (CBT), female, Health care, health care delivery, human, male, Medical problems, panic, Patient monitoring, priority journal, psychotherapy, telecommunication, Telemedicine, Therapeutic alliances, validation process, Video conferencing, videorecording},
pubstate = {published},
tppubtype = {article}
}
Delivering psychotherapy by videoconference could significantly increase the accessibility of empirically validated treatments. The aim of this study was to compare the effectiveness of cognitive-behavior therapy (CBT) for panic disorder with agoraphobia (PDA) when the therapy is delivered either face-to-face or by videoconference. A sample of 21 participants was treated either face-to-face or by videoconference. Results showed that CBT delivered by videoconference was as effective as CBT delivered face-to-face. There was a statistically significant reduction in all measures, and the number of panic-free participants among those receiving CBT by videoconference was 81% at post-treatment and 91% at the 6-month follow-up. None of the comparisons with face-to-face psychotherapy suggested that CBT delivered by videoconference was less effective. These results were confirmed by analyses of effect size. The participants reported the development of an excellent therapeutic alliance in videoconference as early as the first therapy session. The importance of these results for treatment accessibility is discussed. Hypotheses are proposed to explain the rapid creation of strong therapeutic alliances in videoconferencing.