@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}

}

@article{ouyed_group–features_2020,

title = {Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition},

author = {O. Ouyed and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078662750&doi=10.1016%2fj.eswa.2020.113247&partnerID=40&md5=8738cfe1a8a6ded1d5f247b8c62de724},

doi = {10.1016/j.eswa.2020.113247},

issn = {09574174},

year  = {2020},

date = {2020-01-01},

journal = {Expert Systems with Applications},

volume = {148},

publisher = {Elsevier Ltd},

abstract = {We propose an approach for human interaction recognition (HIR) in videos using multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our approach couples kernel and group sparsity modelling to ensure highly precise interaction classification. The group structure in GFR-MKLR is chosen to reflect a representation of interactions at the level of gestures, which ensures more robustness to intra-class variability due to occlusions and changes in subject appearance, body size and viewpoint. The groups consist of motion features extracted from tracking interacting persons joints over time. We encode group sparsity in GFR-MKLR through relevance weights reflecting each group (gesture) discrimination capability between different interaction categories. These weights are automatically estimated during GFR-MKLR training using gradient descent minimisation. Our model is computationally efficient and can be trained on a small training dataset while maintaining a good generalization and interpretation capabilities. Experiments on the well-known UT-Interaction dataset have demonstrated the performance of our approach by comparison with state-of-art methods. © 2020 Elsevier Ltd},

note = {Publisher: Elsevier Ltd},

keywords = {Arts computing, Computationally efficient, Gradient descent, Gradient methods, Group sparsities, Group-of-features relevance, Human interaction recognition, Multinomial kernels, regression analysis, Relevance weights, State-of-art methods},

pubstate = {published},

tppubtype = {article}

}

@article{ouyed_feature_2018,

title = {Feature weighting for multinomial kernel logistic regression and application to action recognition},

author = {O. Ouyed and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035104467&doi=10.1016%2fj.neucom.2017.10.024&partnerID=40&md5=09687b392a405be4338799a750932cf3},

doi = {10.1016/j.neucom.2017.10.024},

issn = {09252312},

year  = {2018},

date = {2018-01-01},

journal = {Neurocomputing},

volume = {275},

pages = {1752–1768},

publisher = {Elsevier B.V.},

abstract = {Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification},

pubstate = {published},

tppubtype = {article}

}

@article{ouyedFeatureWeightingMultinomial2018,

title = {Feature weighting for multinomial kernel logistic regression and application to action recognition},

author = {O. Ouyed and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035104467&doi=10.1016%2fj.neucom.2017.10.024&partnerID=40&md5=09687b392a405be4338799a750932cf3},

doi = {10.1016/j.neucom.2017.10.024},

issn = {09252312},

year  = {2018},

date = {2018-01-01},

journal = {Neurocomputing},

volume = {275},

pages = {1752–1768},

publisher = {Elsevier B.V.},

abstract = {Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.},

keywords = {Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification},

pubstate = {published},

tppubtype = {article}

}

@article{royer_efficient_2016,

title = {Efficient visual information for unfamiliar face matching despite viewpoint variations: It's not in the eyes!},

author = {J. Royer and C. Blais and V. Barnabé-Lortie and M. Carré and J. Leclerc and D. Fiset},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968779426&doi=10.1016%2fj.visres.2016.04.004&partnerID=40&md5=4c63f6eea279f7322c9af23ae9ed22c1},

doi = {10.1016/j.visres.2016.04.004},

issn = {00426989 (ISSN)},

year  = {2016},

date = {2016-01-01},

journal = {Vision Research},

volume = {123},

pages = {33–40},

publisher = {Elsevier Ltd},

abstract = {Faces are encountered in highly diverse angles in real-world settings. Despite this considerable diversity, most individuals are able to easily recognize familiar faces. The vast majority of studies in the field of face recognition have nonetheless focused almost exclusively on frontal views of faces. Indeed, a number of authors have investigated the diagnostic facial features for the recognition of frontal views of faces previously encoded in this same view. However, the nature of the information useful for identity matching when the encoded face and test face differ in viewing angle remains mostly unexplored. The present study addresses this issue using individual differences and bubbles, a method that pinpoints the facial features effectively used in a visual categorization task. Our results indicate that the use of features located in the center of the face, the lower left portion of the nose area and the center of the mouth, are significantly associated with individual efficiency to generalize a face's identity across different viewpoints. However, as faces become more familiar, the reliance on this area decreases, while the diagnosticity of the eye region increases. This suggests that a certain distinction can be made between the visual mechanisms subtending viewpoint invariance and face recognition in the case of unfamiliar face identification. Our results further support the idea that the eye area may only come into play when the face stimulus is particularly familiar to the observer. © 2016 Elsevier Ltd.},

note = {Publisher: Elsevier Ltd},

keywords = {accuracy, adult, article, association, attention, Bubbles, Evoked Potentials, eye fixation, Face, face profile, face recognition, Facial Recognition, facies, female, Fixation, human, human experiment, Humans, Image analysis, Individual differences, male, Ocular, Pattern Recognition, Photic Stimulation, photostimulation, physiology, priority journal, procedures, Psychophysics, recognition, Recognition (Psychology), regression analysis, task performance, unfamiliar face matching, viewpoint variation, Viewpoint variations, Visual, visual discrimination, visual evoked potential, visual information, visual memory, visual stimulation, visual system parameters, Young Adult},

pubstate = {published},

tppubtype = {article}

}