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.
Ziou, D.; Bouguila, N.; Allili, M. S.; El-Zaart, A.
Finite Gamma mixture modelling using minimum message length inference: Application to SAR image analysis Article de journal
Dans: International Journal of Remote Sensing, vol. 30, no 3, p. 771–792, 2009, ISSN: 01431161, (Publisher: Taylor and Francis Ltd.).
Résumé | Liens | BibTeX | Étiquettes: Change detection, Determining the number of clusters, estimation method, finite element method, Finite mixtures, Gamma distribution, Gamma mixtures, Image analysis, Image processing, Image segmentation, Minimum message lengths, Mixtures, Number of clusters, numerical model, Probability distributions, Radar imaging, SAR image segmentation, Synthetic aperture radar, Unsupervised learning
@article{ziou_finite_2009,
title = {Finite Gamma mixture modelling using minimum message length inference: Application to SAR image analysis},
author = {D. Ziou and N. Bouguila and M. S. Allili and A. El-Zaart},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650686123&doi=10.1080%2f01431160802392646&partnerID=40&md5=901ea39ad806dcb62cd630585469af60},
doi = {10.1080/01431160802392646},
issn = {01431161},
year = {2009},
date = {2009-01-01},
journal = {International Journal of Remote Sensing},
volume = {30},
number = {3},
pages = {771–792},
abstract = {This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.},
note = {Publisher: Taylor and Francis Ltd.},
keywords = {Change detection, Determining the number of clusters, estimation method, finite element method, Finite mixtures, Gamma distribution, Gamma mixtures, Image analysis, Image processing, Image segmentation, Minimum message lengths, Mixtures, Number of clusters, numerical model, Probability distributions, Radar imaging, SAR image segmentation, Synthetic aperture radar, Unsupervised learning},
pubstate = {published},
tppubtype = {article}
}
This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.
3.
Allili, M. S.
Effective object tracking by matching object and background models using active contours Article d'actes
Dans: Proceedings - International Conference on Image Processing, ICIP, p. 873–876, IEEE Computer Society, Cairo, 2009, ISBN: 15224880 (ISSN); 978-142445654-3 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP).
Résumé | Liens | BibTeX | Étiquettes: Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking
@inproceedings{allili_effective_2009,
title = {Effective object tracking by matching object and background models using active contours},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951940408&doi=10.1109%2fICIP.2009.5414279&partnerID=40&md5=6838bb85dbef6c9548684a506df3d2b2},
doi = {10.1109/ICIP.2009.5414279},
isbn = {15224880 (ISSN); 978-142445654-3 (ISBN)},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings - International Conference on Image Processing, ICIP},
pages = {873–876},
publisher = {IEEE Computer Society},
address = {Cairo},
abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions. ©2009 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP},
keywords = {Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions. ©2009 IEEE.
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.
Chartier, S.; Giguère, G.; Renaud, P.; Lina, J. -M.; Proulx, R.
FEBAM: A feature-extracting bidirectional associative memory Article d'actes
Dans: IEEE International Conference on Neural Networks - Conference Proceedings, p. 1679–1684, Orlando, FL, 2007, ISBN: 1-4244-1380-X 978-1-4244-1380-5, (ISSN: 10987576).
Résumé | Liens | BibTeX | Étiquettes: Artificial intelligence, Associative processing, Bi-directional associative memory, Blind source separation, Computer networks, Data storage equipment, Feature extraction, Financial data processing, Hemodynamics, Image processing, Image reconstruction, Independent component analysis, Joint conference, Neural networks, Separation
@inproceedings{chartier_febam_2007,
title = {FEBAM: A feature-extracting bidirectional associative memory},
author = {S. Chartier and G. Giguère and P. Renaud and J. -M. Lina and R. Proulx},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-51749114880&doi=10.1109%2fIJCNN.2007.4371210&partnerID=40&md5=d929ff11969da516dec3fb7a11e742a1},
doi = {10.1109/IJCNN.2007.4371210},
isbn = {1-4244-1380-X 978-1-4244-1380-5},
year = {2007},
date = {2007-01-01},
booktitle = {IEEE International Conference on Neural Networks - Conference Proceedings},
pages = {1679–1684},
address = {Orlando, FL},
abstract = {In this paper, a new model that can ultimately create its own set of perceptual features is proposed. Using a bidirectional associative memory (BAM)-inspired architecture, the resulting model inherits properties such as attractor-like behavior and successful processing of noisy inputs, while being able to achieve principal component analysis (PCA) tasks such as feature extraction and dimensionality reduction. The model is tested by simulating image reconstruction and blind source separation tasks. Simulations show that the model fares particularly well compared to current neural PCA and independent component analysis (ICA) algorithms. It is argued the model possesses more cognitive explanative power than any other nonlinear/linear PCA and ICA algorithm. ©2007 IEEE.},
note = {ISSN: 10987576},
keywords = {Artificial intelligence, Associative processing, Bi-directional associative memory, Blind source separation, Computer networks, Data storage equipment, Feature extraction, Financial data processing, Hemodynamics, Image processing, Image reconstruction, Independent component analysis, Joint conference, Neural networks, Separation},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, a new model that can ultimately create its own set of perceptual features is proposed. Using a bidirectional associative memory (BAM)-inspired architecture, the resulting model inherits properties such as attractor-like behavior and successful processing of noisy inputs, while being able to achieve principal component analysis (PCA) tasks such as feature extraction and dimensionality reduction. The model is tested by simulating image reconstruction and blind source separation tasks. Simulations show that the model fares particularly well compared to current neural PCA and independent component analysis (ICA) algorithms. It is argued the model possesses more cognitive explanative power than any other nonlinear/linear PCA and ICA algorithm. ©2007 IEEE.
6.
Allili, M. S.; Ziou, D.
A robust video object tracking by using active contours Article d'actes
Dans: 2006 Conference on Computer Vision and Pattern Recognition Workshops, p. 135, IEEE Computer Society, New York, NY, 2006, ISBN: 0769526462 (ISBN); 978-076952646-1 (ISBN), (Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops).
Résumé | Liens | BibTeX | Étiquettes: Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking
@inproceedings{allili_robust_2006,
title = {A robust video object tracking by using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845513941&doi=10.1109%2fCVPRW.2006.20&partnerID=40&md5=64ff2be5c45a6c206420bf6eb5589bca},
doi = {10.1109/CVPRW.2006.20},
isbn = {0769526462 (ISBN); 978-076952646-1 (ISBN)},
year = {2006},
date = {2006-01-01},
booktitle = {2006 Conference on Computer Vision and Pattern Recognition Workshops},
volume = {2006},
pages = {135},
publisher = {IEEE Computer Society},
address = {New York, NY},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.},
note = {Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops},
keywords = {Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.