Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Côté, S. S. -P.; Brideau-Duquette, M.; Lafortune, D.; Pfaus, J. G.; Renaud, P.
Dans: O., Poquet; A., Ortega-Arranz; O., Viberg; I.-A., Chounta; B., McLaren; J., Jovanovic (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, p. 694–700, Science and Technology Publications, Lda, 2024, ISBN: 21845026 (ISSN); 978-989758697-2 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Résumé | Liens | BibTeX | Étiquettes: Affordances, Behavioral measures, Computer vision, E-learning, Electroencephalography, Electrophysiology, Gaze Behaviour, Gaze behaviours, Immersive, Learning, Physiological measures, Quantitative electroencephalography, Quantitative Electroencephalography (qEEG), Sexual Presence, Therapeutic Application, Vaginal Photoplethysmography, virtual reality
@inproceedings{cote_investigating_2024,
title = {Investigating Female Sexual Presence Through Triangulation of Behavioral and Physiological Measures in Virtual Reality: Towards Therapeutic Applications for Sexual Disorders},
author = {S. S. -P. Côté and M. Brideau-Duquette and D. Lafortune and J. G. Pfaus and P. Renaud},
editor = {Poquet O. and Ortega-Arranz A. and Viberg O. and Chounta I.-A. and McLaren B. and Jovanovic J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193914916&doi=10.5220%2f0012754700003693&partnerID=40&md5=435ba537cddf1277ed1b459b8a0b1984},
doi = {10.5220/0012754700003693},
isbn = {21845026 (ISSN); 978-989758697-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {694–700},
publisher = {Science and Technology Publications, Lda},
abstract = {Exposure to sexual contexts by means of immersive, extended reality technologies, offer an opportunity to both: better understand sexual responding, and in turn, offers insights as to how the same technology could help in treating sexual disorders. The present papers reports on the ability of behavioural (i.e., oculometry) and physiological (i.e., electroencephalography and vaginal plethysmography) to conjointly predict subjective sexual feelings (i.e., subjective sexual presence), this, using a sample of 12 heterosexual cisgendered women. Measurements pertained to the participants living a sexual immersion (via a virtual reality headset) with an opposite sex virtual character engaging in sexually suggestive behaviour. Results suggest that all the tested behavioural and physiological measurements could play a role in the shaping of sexual presence. Results are discussed with therapeutic learning processes considerations in mind. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Affordances, Behavioral measures, Computer vision, E-learning, Electroencephalography, Electrophysiology, Gaze Behaviour, Gaze behaviours, Immersive, Learning, Physiological measures, Quantitative electroencephalography, Quantitative Electroencephalography (qEEG), Sexual Presence, Therapeutic Application, Vaginal Photoplethysmography, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Exposure to sexual contexts by means of immersive, extended reality technologies, offer an opportunity to both: better understand sexual responding, and in turn, offers insights as to how the same technology could help in treating sexual disorders. The present papers reports on the ability of behavioural (i.e., oculometry) and physiological (i.e., electroencephalography and vaginal plethysmography) to conjointly predict subjective sexual feelings (i.e., subjective sexual presence), this, using a sample of 12 heterosexual cisgendered women. Measurements pertained to the participants living a sexual immersion (via a virtual reality headset) with an opposite sex virtual character engaging in sexually suggestive behaviour. Results suggest that all the tested behavioural and physiological measurements could play a role in the shaping of sexual presence. Results are discussed with therapeutic learning processes considerations in mind. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
2.
Larivière, G.; Allili, M. S.
A learning probabilistic approach for object segmentation Article d'actes
Dans: Proceedings of the 2012 9th Conference on Computer and Robot Vision, CRV 2012, p. 86–93, Toronto, ON, 2012, ISBN: 978-076954683-4 (ISBN), (Journal Abbreviation: Proc. Conf. Comput. Rob. Vis., CRV).
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Computer vision, fragments, Image segmentation, Mean shift algorithm, mean-shift algorithm, Object recognition, Object segmentation, Object shape, Optimal segmentation, Probabilistic approaches, Probabilistic Learning, Segmentation process
@inproceedings{lariviere_learning_2012,
title = {A learning probabilistic approach for object segmentation},
author = {G. Larivière and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878376248&doi=10.1109%2fCRV.2012.19&partnerID=40&md5=044a531d9d6de8036a434993f7b5d7ba},
doi = {10.1109/CRV.2012.19},
isbn = {978-076954683-4 (ISBN)},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the 2012 9th Conference on Computer and Robot Vision, CRV 2012},
pages = {86–93},
address = {Toronto, ON},
abstract = {This paper proposes a new method for figure-ground image segmentation based on a probabilistic learning approach of the object shape. Historically, segmentation is mostly defined as a data-driven bottom-up process, where pixels are grouped into regions/objects according to objective criteria, such as region homogeneity, etc. In particular, it aims at creating a partition of the image into contiguous, homogenous regions. In the proposed work, we propose to incorporate prior knowledge about the object shape and category to segment the object from the background. The segmentation process is composed of two parts. In the first part, object shape models are built using sets of object fragments. The second part starts by first segmenting an image into homogenous regions using the mean-shift algorithm. Then, several object hypotheses are tested and validated using the different object shape models as supporting information. As an output, our algorithm identifies the object category, position, as well as its optimal segmentation. Experimental results show the capacity of the approach to segment several object categories. © 2012 IEEE.},
note = {Journal Abbreviation: Proc. Conf. Comput. Rob. Vis., CRV},
keywords = {Algorithms, Computer vision, fragments, Image segmentation, Mean shift algorithm, mean-shift algorithm, Object recognition, Object segmentation, Object shape, Optimal segmentation, Probabilistic approaches, Probabilistic Learning, Segmentation process},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a new method for figure-ground image segmentation based on a probabilistic learning approach of the object shape. Historically, segmentation is mostly defined as a data-driven bottom-up process, where pixels are grouped into regions/objects according to objective criteria, such as region homogeneity, etc. In particular, it aims at creating a partition of the image into contiguous, homogenous regions. In the proposed work, we propose to incorporate prior knowledge about the object shape and category to segment the object from the background. The segmentation process is composed of two parts. In the first part, object shape models are built using sets of object fragments. The second part starts by first segmenting an image into homogenous regions using the mean-shift algorithm. Then, several object hypotheses are tested and validated using the different object shape models as supporting information. As an output, our algorithm identifies the object category, position, as well as its optimal segmentation. Experimental results show the capacity of the approach to segment several object categories. © 2012 IEEE.
3.
Allili, M. S.; Ziou, D.; Bouguila, N.; Boutemedjet, S.
Unsupervised feature selection and learning for image segmentation Article d'actes
Dans: CRV 2010 - 7th Canadian Conference on Computer and Robot Vision, p. 285–292, Ottawa, ON, 2010, ISBN: 978-0-7695-4040-5.
Résumé | Liens | BibTeX | Étiquettes: Clustering algorithms, Computer vision, Evolutionary algorithms, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Generalized Gaussian Distributions, Heavy-tailed, High dimensional spaces, Image distributions, Image segmentation, Large database, Over-estimation, Real-world image, Unsupervised feature selection
@inproceedings{allili_unsupervised_2010,
title = {Unsupervised feature selection and learning for image segmentation},
author = {M. S. Allili and D. Ziou and N. Bouguila and S. Boutemedjet},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954407977&doi=10.1109%2fCRV.2010.44&partnerID=40&md5=a7d8e3147216429f18ef7af3167acb42},
doi = {10.1109/CRV.2010.44},
isbn = {978-0-7695-4040-5},
year = {2010},
date = {2010-01-01},
booktitle = {CRV 2010 - 7th Canadian Conference on Computer and Robot Vision},
pages = {285–292},
address = {Ottawa, ON},
abstract = {In this paper we investigate the integration of feature selection in segmentation through an unsupervised learning approach. We propose a clustering algorithm that efficiently mitigates image under/over-segmentation, by combining generalized Gaussian mixture modeling and feature selection. The algorithm is based on generalized Gaussian mixture modeling which is less prone to region number over-estimation in case of noisy and heavy-tailed image distributions. On the other hand, our feature selection mechanism allows to automatically discard uninformative features, which leads to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a large database of real-world images showed us the effectiveness of the proposed approach. © 2010 IEEE.},
keywords = {Clustering algorithms, Computer vision, Evolutionary algorithms, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Generalized Gaussian Distributions, Heavy-tailed, High dimensional spaces, Image distributions, Image segmentation, Large database, Over-estimation, Real-world image, Unsupervised feature selection},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we investigate the integration of feature selection in segmentation through an unsupervised learning approach. We propose a clustering algorithm that efficiently mitigates image under/over-segmentation, by combining generalized Gaussian mixture modeling and feature selection. The algorithm is based on generalized Gaussian mixture modeling which is less prone to region number over-estimation in case of noisy and heavy-tailed image distributions. On the other hand, our feature selection mechanism allows to automatically discard uninformative features, which leads to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a large database of real-world images showed us the effectiveness of the proposed approach. © 2010 IEEE.
4.
Allili, M. S.
Object contour tracking using foreground and background distribution matching Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5856 LNCS, p. 954–961, 2009, ISSN: 03029743, (ISBN: 3642102670; 9783642102677 Place: Guadalajara, Jalisco).
Résumé | Liens | BibTeX | Étiquettes: Active contours, Computer applications, Computer vision, Distribution matching, Distribution parameters, Image matching, Object contour, Tracked objects
@article{allili_object_2009,
title = {Object contour tracking using foreground and background distribution matching},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651256419&doi=10.1007%2f978-3-642-10268-4_111&partnerID=40&md5=0852d2cf799d98cff187d1b10b2e5c34},
doi = {10.1007/978-3-642-10268-4_111},
issn = {03029743},
year = {2009},
date = {2009-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5856 LNCS},
pages = {954–961},
abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked object and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. The object contour is tracked by assigning pixels in a way that maximizes the likelihood of the object versus the background. We implement the approach using an EM-like algorithm which evolves the object contour exactly to its boundaries and adapts the distribution parameters of the object and the background to data. © 2009 Springer-Verlag Berlin Heidelberg.},
note = {ISBN: 3642102670; 9783642102677
Place: Guadalajara, Jalisco},
keywords = {Active contours, Computer applications, Computer vision, Distribution matching, Distribution parameters, Image matching, Object contour, Tracked objects},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked object and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. The object contour is tracked by assigning pixels in a way that maximizes the likelihood of the object versus the background. We implement the approach using an EM-like algorithm which evolves the object contour exactly to its boundaries and adapts the distribution parameters of the object and the background to data. © 2009 Springer-Verlag Berlin Heidelberg.
5.
Allili, M. S.; Ziou, D.
An automatic segmentation combining mixture analysis and adaptive region information: A level set approach Article d'actes
Dans: Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005, p. 73–80, Institute of Electrical and Electronics Engineers Inc., Genova, 2005, ISBN: 0769523196 (ISBN); 978-076952319-4 (ISBN), (Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV).
Résumé | Liens | BibTeX | Étiquettes: Adaptive segmentation, Automatic segmentations, Color image processing, Color image segmentation, Computer vision, Energy functionals, Image segmentation, Level Set, Level sets, Mixture analysis, Mixtures, Polarity smoothing, Posterior probability
@inproceedings{allili_automatic_2005-1,
title = {An automatic segmentation combining mixture analysis and adaptive region information: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845531999&doi=10.1109%2fCRV.2005.14&partnerID=40&md5=c9773a2f28fe00b4171511895b721158},
doi = {10.1109/CRV.2005.14},
isbn = {0769523196 (ISBN); 978-076952319-4 (ISBN)},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005},
volume = {1},
pages = {73–80},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
address = {Genova},
abstract = {In this paper, we propose a novel automatic framework for variational color image segmentation based on unifying adaptive region information and mixture modelling. We consider a formulation of the region information by using the posterior probability of a mixture of general Gaussian (GG) pdfs, where each region is represented by a pdf. The segmentation is formulated by the minimization of an energy functional according to the region contours and all the mixture parameters respectively. Two main objectives are achieved by the approach. A scheme is provided to extend easily the adaptive segmentation to an arbitrary number of regions and to perform it in a fully automatic fashion. Moreover, the segmentation recovers an accurate and representative mixture of pdfs. In the approach, we couple the boundary and region information of the image to steer the segmentation. We validate the method on the segmentation of real world color images. © 2005 IEEE.},
note = {Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV},
keywords = {Adaptive segmentation, Automatic segmentations, Color image processing, Color image segmentation, Computer vision, Energy functionals, Image segmentation, Level Set, Level sets, Mixture analysis, Mixtures, Polarity smoothing, Posterior probability},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel automatic framework for variational color image segmentation based on unifying adaptive region information and mixture modelling. We consider a formulation of the region information by using the posterior probability of a mixture of general Gaussian (GG) pdfs, where each region is represented by a pdf. The segmentation is formulated by the minimization of an energy functional according to the region contours and all the mixture parameters respectively. Two main objectives are achieved by the approach. A scheme is provided to extend easily the adaptive segmentation to an arbitrary number of regions and to perform it in a fully automatic fashion. Moreover, the segmentation recovers an accurate and representative mixture of pdfs. In the approach, we couple the boundary and region information of the image to steer the segmentation. We validate the method on the segmentation of real world color images. © 2005 IEEE.