Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Journal Article
In: IEEE Open Journal of the Computer Society, vol. 7, pp. 492–503, 2026, ISSN: 26441268 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadeh_vision-based_2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
2.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks Journal Article
In: IEEE Transactions on Artificial Intelligence, vol. 7, no. 6, pp. 3146–3159, 2026, ISSN: 26914581 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing
@article{abdollahzadehVisualSafetyMapping2026,
title = {Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/pages/publications/105023324811?origin=resultslist},
doi = {10.1109/TAI.2025.3635093},
issn = {26914581 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Transactions on Artificial Intelligence},
volume = {7},
number = {6},
pages = {3146–3159},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing},
pubstate = {published},
tppubtype = {article}
}
As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.
3.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Journal Article
In: IEEE Open Journal of the Computer Society, vol. 7, pp. 492–503, 2026, ISSN: 26441268 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadehVisionBasedFrameworkSafe2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/pages/publications/105029942397?origin=resultslist},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
4.
Hamadene, A.; Allili, M. S.
Cross-Model Deepfake Detection Through Contourlet-Based Inter-Channel Spectral Analysis Journal Article
In: IEEE Transactions on Biometrics, Behavior, and Identity Science, 2026, ISSN: 26376407 (ISSN).
Abstract | Links | BibTeX | Tags: Attribution, Computer forensics, Computer vision, Contourlet transform, Contourlets, Cross model, Cross-Model Generalization, Deep learning, Deepfake detection, Forensic engineering, Image Enhancement, Inter-channel contourlet feature, Inter-Channel Contourlet Features, Model generalization, Performance, Public trust, Spectral analyze, Spectrum analysis
@article{hamadeneCrossModelDeepfakeDetection2026,
title = {Cross-Model Deepfake Detection Through Contourlet-Based Inter-Channel Spectral Analysis},
author = {A. Hamadene and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105038690534?origin=resultslist},
doi = {10.1109/TBIOM.2026.3687469},
issn = {26376407 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid advancement of generative AI has fueled the proliferation of highly realistic deepfakes, raising growing concerns for public trust, digital forensics, and societal stability. While deep learning-based detectors have shown promising results in controlled settings, their effectiveness diminishes significantly across different datasets or unseen deepfakes. This performance drop reveals a core limitation: overfitting to dataset-specific artifacts and a failure to learn robust, content-agnostic forensic cues. In this work, we study a novel deepfake characterization that targets inter-channel spectral inconsistencies, a subtle but critical artifact of AI-generated imagery. Departing from conventional approaches that focus on spatial irregularities or within-channel frequency analysis, our method leverages the Contourlet Transform (CT) to extract high-frequency, multiscale, and multidirectional features across color channels. These features reveal latent spectral distortions and structural misalignments that persist across different generative models, including both GANs and Diffusion Models. Extensive experiments show that our approach generalizes well across datasets, achieving strong performance in both model-specific and cross-model detection tasks. Our findings highlight the promise of inter-channel spectral analysis for advancing the robustness and generalizability of deepfake detection systems. © 2019 IEEE.},
keywords = {Attribution, Computer forensics, Computer vision, Contourlet transform, Contourlets, Cross model, Cross-Model Generalization, Deep learning, Deepfake detection, Forensic engineering, Image Enhancement, Inter-channel contourlet feature, Inter-Channel Contourlet Features, Model generalization, Performance, Public trust, Spectral analyze, Spectrum analysis},
pubstate = {published},
tppubtype = {article}
}
The rapid advancement of generative AI has fueled the proliferation of highly realistic deepfakes, raising growing concerns for public trust, digital forensics, and societal stability. While deep learning-based detectors have shown promising results in controlled settings, their effectiveness diminishes significantly across different datasets or unseen deepfakes. This performance drop reveals a core limitation: overfitting to dataset-specific artifacts and a failure to learn robust, content-agnostic forensic cues. In this work, we study a novel deepfake characterization that targets inter-channel spectral inconsistencies, a subtle but critical artifact of AI-generated imagery. Departing from conventional approaches that focus on spatial irregularities or within-channel frequency analysis, our method leverages the Contourlet Transform (CT) to extract high-frequency, multiscale, and multidirectional features across color channels. These features reveal latent spectral distortions and structural misalignments that persist across different generative models, including both GANs and Diffusion Models. Extensive experiments show that our approach generalizes well across datasets, achieving strong performance in both model-specific and cross-model detection tasks. Our findings highlight the promise of inter-channel spectral analysis for advancing the robustness and generalizability of deepfake detection systems. © 2019 IEEE.
5.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Journal Article
In: IEEE Open Journal of the Computer Society, vol. 7, pp. 492–503, 2026, ISSN: 26441268 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadehVisionBasedFrameworkSafe2026a,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
6.
Jalleli, O.; Zhu, Y.; Falk, T. H.
Audio-Visual Cross-Attention for Improved Deepfake Video Detection and Forgery Localization Proceedings Article
In: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., pp. 75–79, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Audio acoustics, Audio signal processing, Audio-visual, Computer vision, Crossmodal attention, Deepfake detection, Forgery, Generative AI, Localisation, Multi-modal, Video detection, Video forgeries, Visual modalities
@inproceedings{jalleliAudioVisualCrossAttentionImproved2025,
title = {Audio-Visual Cross-Attention for Improved Deepfake Video Detection and Forgery Localization},
author = {O. Jalleli and Y. Zhu and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105033150623?origin=resultslist},
doi = {10.1109/SMC58881.2025.11342953},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {75–79},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {With the emergence of multi-modal generative models, synthesized videos are becoming increasingly realistic, making the detection of deepfakes extremely challenging. While several video deepfake detection models have shown promising performance, their focus has been primarily on the visual modality. To overcome this limitation, we propose a dualstream framework that fuses visual and auditory information via cross-attention computed between embeddings extracted from pre-trained video and audio encoders. Additionally, we design a weakly-supervised forgery localization head that infers frame-level forgery scores from coarse segment-level labels, minimizing the need for fine-grained annotations and allowing for forgery location characterization. In this paper, we describe our preliminary results showing the proposed model outperforming state-of-the-art detectors on both frame-level localization and sequence-level deepfake detection tasks. Ongoing work focuses on investigating the complementarity between the visual and auditory modalities to improve model robustness and explainability. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Artificial intelligence, Audio acoustics, Audio signal processing, Audio-visual, Computer vision, Crossmodal attention, Deepfake detection, Forgery, Generative AI, Localisation, Multi-modal, Video detection, Video forgeries, Visual modalities},
pubstate = {published},
tppubtype = {inproceedings}
}
With the emergence of multi-modal generative models, synthesized videos are becoming increasingly realistic, making the detection of deepfakes extremely challenging. While several video deepfake detection models have shown promising performance, their focus has been primarily on the visual modality. To overcome this limitation, we propose a dualstream framework that fuses visual and auditory information via cross-attention computed between embeddings extracted from pre-trained video and audio encoders. Additionally, we design a weakly-supervised forgery localization head that infers frame-level forgery scores from coarse segment-level labels, minimizing the need for fine-grained annotations and allowing for forgery location characterization. In this paper, we describe our preliminary results showing the proposed model outperforming state-of-the-art detectors on both frame-level localization and sequence-level deepfake detection tasks. Ongoing work focuses on investigating the complementarity between the visual and auditory modalities to improve model robustness and explainability. © 2025 IEEE.
7.
Côté, S. S. -P.; Brideau-Duquette, M.; Lafortune, D.; Pfaus, J. G.; Renaud, P.
Investigating Female Sexual Presence Through Triangulation of Behavioral and Physiological Measures in Virtual Reality: Towards Therapeutic Applications for Sexual Disorders Proceedings Article
In: O., Poquet; A., Ortega-Arranz; O., Viberg; I.-A., Chounta; B., McLaren; J., Jovanovic (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 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).
Abstract | Links | BibTeX | Tags: 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.
8.
Côté, S. S. -P.; Brideau-Duquette, M.; Lafortune, D.; Pfaus, J. G.; Renaud, P.
Investigating Female Sexual Presence Through Triangulation of Behavioral and Physiological Measures in Virtual Reality: Towards Therapeutic Applications for Sexual Disorders Proceedings Article
In: O., Poquet; A., Ortega-Arranz; O., Viberg; I.-A., Chounta; B., McLaren; J., Jovanovic (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 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).
Abstract | Links | BibTeX | Tags: 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{coteInvestigatingFemaleSexual2024,
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.
9.
Larivière, G.; Allili, M. S.
A learning probabilistic approach for object segmentation Proceedings Article
In: Proceedings of the 2012 9th Conference on Computer and Robot Vision, CRV 2012, pp. 86–93, Toronto, ON, 2012, ISBN: 978-076954683-4 (ISBN), (Journal Abbreviation: Proc. Conf. Comput. Rob. Vis., CRV).
Abstract | Links | BibTeX | Tags: 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.
10.
Larivière, G.; Allili, M. S.
A learning probabilistic approach for object segmentation Proceedings Article
In: Proceedings of the 2012 9th Conference on Computer and Robot Vision, CRV 2012, pp. 86–93, Toronto, ON, 2012, ISBN: 978-076954683-4 (ISBN), (Journal Abbreviation: Proc. Conf. Comput. Rob. Vis., CRV).
Abstract | Links | BibTeX | Tags: 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{lariviereLearningProbabilisticApproach2012,
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.