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.
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.
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{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.
4.
Zhang, H.; Xu, Y.; Tian, Y.; Li, Y.; Falk, T. H.; Wang, F. -Y.
Selective Shift: Towards Personalized Domain Adaptation in Multi-Agent Collaborative Perception Proceedings Article
In: MM - Proc. ACM Int. Conf. Multimedia, Co-Located with MM, pp. 886–895, Association for Computing Machinery, Inc, 2025, ISBN: 979-840072035-2 (ISBN), (Journal Abbreviation: MM - Proc. ACM Int. Conf. Multimedia, Co-Located with MM).
Abstract | Links | BibTeX | Tags: 3D object, 3D object detection, Adaptation methods, Adaptive alignment, Collaborative perception, Domain adaptation, Entropy, Intelligent agents, Multi agent, Multi agent systems, Object detection, Object recognition, Objects detection, Relational semantics, Semantics, uncertainty
@inproceedings{zhangSelectiveShiftPersonalized2025,
title = {Selective Shift: Towards Personalized Domain Adaptation in Multi-Agent Collaborative Perception},
author = {H. Zhang and Y. Xu and Y. Tian and Y. Li and T. H. Falk and F. -Y. Wang},
url = {https://www.scopus.com/pages/publications/105024076483?origin=resultslist},
doi = {10.1145/3746027.3754723},
isbn = {979-840072035-2 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {MM - Proc. ACM Int. Conf. Multimedia, Co-Located with MM},
pages = {886–895},
publisher = {Association for Computing Machinery, Inc},
abstract = {Given the scarcity of real data and the time-intensive nature of labeling, current multi-agent perception models often rely on simulated sensor data for training and validation. However, perception performance deteriorates significantly due to domain gap between simulated and real data. Existing adaptation methods focus on domain-generalized feature extraction while neglecting multi-agent shift uncertainty and relational semantic loss. To address this issue, we propose a Selective Shift Domain Adaptation method in multi-agent collaborative perception, called SSDA. SSDA incorporates two essential components: the frequency-decoupled feature shift adjustment (FSA) and the entropy-driven staged adaptive alignment (SAA). To mitigate the relational semantic loss, the FSA is proposed to simplify the representation of correlation features and remove redundant information from the source domain, thereby mitigating interference for domain adversarial scenarios. To tackle the shift uncertainty, the SAA is designed to achieve adaptive alignment from global to local guided by information entropy, which dynamically adjusts weights for samples according to their level of uncertainty. The results demonstrate that the SSDA is significantly superior to the SOTA, achieving up to 7.35% improvements on AP@0.7. © 2025 ACM.},
note = {Journal Abbreviation: MM - Proc. ACM Int. Conf. Multimedia, Co-Located with MM},
keywords = {3D object, 3D object detection, Adaptation methods, Adaptive alignment, Collaborative perception, Domain adaptation, Entropy, Intelligent agents, Multi agent, Multi agent systems, Object detection, Object recognition, Objects detection, Relational semantics, Semantics, uncertainty},
pubstate = {published},
tppubtype = {inproceedings}
}
Given the scarcity of real data and the time-intensive nature of labeling, current multi-agent perception models often rely on simulated sensor data for training and validation. However, perception performance deteriorates significantly due to domain gap between simulated and real data. Existing adaptation methods focus on domain-generalized feature extraction while neglecting multi-agent shift uncertainty and relational semantic loss. To address this issue, we propose a Selective Shift Domain Adaptation method in multi-agent collaborative perception, called SSDA. SSDA incorporates two essential components: the frequency-decoupled feature shift adjustment (FSA) and the entropy-driven staged adaptive alignment (SAA). To mitigate the relational semantic loss, the FSA is proposed to simplify the representation of correlation features and remove redundant information from the source domain, thereby mitigating interference for domain adversarial scenarios. To tackle the shift uncertainty, the SAA is designed to achieve adaptive alignment from global to local guided by information entropy, which dynamically adjusts weights for samples according to their level of uncertainty. The results demonstrate that the SSDA is significantly superior to the SOTA, achieving up to 7.35% improvements on AP@0.7. © 2025 ACM.
5.
Audet, F.; Allili, M. S.; Cretu, A. -M.
Salient object detection in images by combining objectness clues in the RGBD space Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10317 LNCS, pp. 247–255, 2017, ISSN: 03029743, (ISBN: 9783319598758).
Abstract | Links | BibTeX | Tags: Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines
@article{audet_salient_2017,
title = {Salient object detection in images by combining objectness clues in the RGBD space},
author = {F. Audet and M. S. Allili and A. -M. Cretu},
editor = {Campilho A. Karray F. Cheriet F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229105&doi=10.1007%2f978-3-319-59876-5_28&partnerID=40&md5=d78eb69cecd0a34ca2d517cfee44ef54},
doi = {10.1007/978-3-319-59876-5_28},
issn = {03029743},
year = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10317 LNCS},
pages = {247–255},
publisher = {Springer Verlag},
abstract = {We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.},
note = {ISBN: 9783319598758},
keywords = {Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.
6.
Audet, F.; Allili, M. S.; Cretu, A. -M.
Salient object detection in images by combining objectness clues in the RGBD space Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10317 LNCS, pp. 247–255, 2017, ISSN: 03029743, (ISBN: 9783319598758).
Abstract | Links | BibTeX | Tags: Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines
@article{audetSalientObjectDetection2017,
title = {Salient object detection in images by combining objectness clues in the RGBD space},
author = {F. Audet and M. S. Allili and A. -M. Cretu},
editor = {Campilho A. Karray F. Cheriet F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229105&doi=10.1007%2f978-3-319-59876-5_28&partnerID=40&md5=d78eb69cecd0a34ca2d517cfee44ef54},
doi = {10.1007/978-3-319-59876-5_28},
issn = {03029743},
year = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10317 LNCS},
pages = {247–255},
publisher = {Springer Verlag},
abstract = {We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.},
note = {ISBN: 9783319598758},
keywords = {Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.
7.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-graph based salient object detection Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, pp. 318–324, 2016, ISSN: 03029743, (ISBN: 9783319415000).
Abstract | Links | BibTeX | Tags: Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects
@article{filali_multi-graph_2016,
title = {Multi-graph based salient object detection},
author = {I. Filali and M. S. Allili and N. Benblidia},
editor = {Karray F. Campilho A. Campilho A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},
doi = {10.1007/978-3-319-41501-7_36},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9730},
pages = {318–324},
publisher = {Springer Verlag},
abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},
note = {ISBN: 9783319415000},
keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.
8.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-scale salient object detection using graph ranking and global–local saliency refinement Journal Article
In: Signal Processing: Image Communication, vol. 47, pp. 380–401, 2016, ISSN: 09235965, (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection
@article{filali_multi-scale_2016,
title = {Multi-scale salient object detection using graph ranking and global–local saliency refinement},
author = {I. Filali and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982091007&doi=10.1016%2fj.image.2016.07.007&partnerID=40&md5=60dabe68b5cff4b5d00216d6a632e1cd},
doi = {10.1016/j.image.2016.07.007},
issn = {09235965},
year = {2016},
date = {2016-01-01},
journal = {Signal Processing: Image Communication},
volume = {47},
pages = {380–401},
publisher = {Elsevier B.V.},
abstract = {We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.},
note = {Publisher: Elsevier B.V.},
keywords = {Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection},
pubstate = {published},
tppubtype = {article}
}
We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.
9.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-graph based salient object detection Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, pp. 318–324, 2016, ISSN: 03029743, (ISBN: 9783319415000).
Abstract | Links | BibTeX | Tags: Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects
@article{filaliMultigraphBasedSalient2016,
title = {Multi-graph based salient object detection},
author = {I. Filali and M. S. Allili and N. Benblidia},
editor = {Karray F. Campilho A. Campilho A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},
doi = {10.1007/978-3-319-41501-7_36},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9730},
pages = {318–324},
publisher = {Springer Verlag},
abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},
note = {ISBN: 9783319415000},
keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.
10.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-scale salient object detection using graph ranking and global–local saliency refinement Journal Article
In: Signal Processing: Image Communication, vol. 47, pp. 380–401, 2016, ISSN: 09235965.
Abstract | Links | BibTeX | Tags: Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection
@article{filaliMultiscaleSalientObject2016,
title = {Multi-scale salient object detection using graph ranking and global–local saliency refinement},
author = {I. Filali and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982091007&doi=10.1016%2fj.image.2016.07.007&partnerID=40&md5=60dabe68b5cff4b5d00216d6a632e1cd},
doi = {10.1016/j.image.2016.07.007},
issn = {09235965},
year = {2016},
date = {2016-01-01},
journal = {Signal Processing: Image Communication},
volume = {47},
pages = {380–401},
publisher = {Elsevier B.V.},
abstract = {We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.},
keywords = {Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection},
pubstate = {published},
tppubtype = {article}
}
We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.