Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Hebbache, L.; Amirkhani, D.; Allili, M. S.; Hammouche, N.; Lapointe, J. -F.
Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery Journal Article
In: Remote Sensing, vol. 15, no. 5, 2023, ISSN: 20724292, (Publisher: MDPI).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Aircraft detection, Antennas, Computational efficiency, Concrete defects, Deep learning, Defect detection, extraction, Feature extraction, Features extraction, Image acquisition, Image Enhancement, Multi-labels, One-stage concrete defect detection, Saliency, Single stage, Unmanned aerial vehicles (UAV), Unmanned areal vehicle imagery
@article{hebbache_leveraging_2023,
title = {Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery},
author = {L. Hebbache and D. Amirkhani and M. S. Allili and N. Hammouche and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149966766&doi=10.3390%2frs15051218&partnerID=40&md5=7bf1cb3353270c696c07ff24dc24655d},
doi = {10.3390/rs15051218},
issn = {20724292},
year = {2023},
date = {2023-01-01},
journal = {Remote Sensing},
volume = {15},
number = {5},
abstract = {Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection. © 2023 by the authors.},
note = {Publisher: MDPI},
keywords = {Aerial vehicle, Aircraft detection, Antennas, Computational efficiency, Concrete defects, Deep learning, Defect detection, extraction, Feature extraction, Features extraction, Image acquisition, Image Enhancement, Multi-labels, One-stage concrete defect detection, Saliency, Single stage, Unmanned aerial vehicles (UAV), Unmanned areal vehicle imagery},
pubstate = {published},
tppubtype = {article}
}
Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection. © 2023 by the authors.
2.
Cote, S. S. -P.; Paquette, G. R.; Neveu, S. -M.; Chartier, S.; Labbe, D. R.; Renaud, P.
Combining electroencephalography with plethysmography for classification of deviant sexual preferences. Proceedings Article
In: Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-172819556-8 (ISBN), (Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF).
Abstract | Links | BibTeX | Tags: Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction
@inproceedings{cote_combining_2021,
title = {Combining electroencephalography with plethysmography for classification of deviant sexual preferences.},
author = {S. S. -P. Cote and G. R. Paquette and S. -M. Neveu and S. Chartier and D. R. Labbe and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113855965&doi=10.1109%2fIWBF50991.2021.9465078&partnerID=40&md5=b545b2a6d22e32115ac179399188960e},
doi = {10.1109/IWBF50991.2021.9465078},
isbn = {978-172819556-8 (ISBN)},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.},
note = {Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF},
keywords = {Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.
3.
Charbonneau, I.; Robinson, K.; Blais, C.; Fiset, D.
Implicit race attitudes modulate visual information extraction for trustworthiness judgments Journal Article
In: PLoS ONE, vol. 15, no. 9 September, 2020, ISSN: 19326203, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: adult, African American, African Americans, article, Attitude, Caucasian, decision making, Ethics, European Continental Ancestry Group, extraction, eyelash, Facial Expression, facies, female, human, Humans, Judgment, male, perception, physiology, psychology, Racism, Social Perception, Stereotyping, visual information, wrinkle, Young Adult
@article{charbonneau_implicit_2020,
title = {Implicit race attitudes modulate visual information extraction for trustworthiness judgments},
author = {I. Charbonneau and K. Robinson and C. Blais and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091622106&doi=10.1371%2fjournal.pone.0239305&partnerID=40&md5=18ca2332affc9cb41d17afc8c450b0b4},
doi = {10.1371/journal.pone.0239305},
issn = {19326203},
year = {2020},
date = {2020-01-01},
journal = {PLoS ONE},
volume = {15},
number = {9 September},
abstract = {Black people are still considered to be one of the most stigmatized groups and have to face multiple prejudices that undermine their well-being. Assumptions and beliefs about other racial groups are quite pervasive and have been shown to impact basic social tasks such as face processing. For example, individuals with high racial prejudice conceptualize other-race faces as less trustworthy and more criminal. However, it is unknown if implicit racial bias could modulate even low-level perceptual mechanisms such as spatial frequency (SF) extraction when judging the level of trustworthiness of other-race faces. The present study showed that although similar facial features are used to judge the trustworthiness of White and Black faces, own-race faces are processed in lower SF (i.e. coarse information such as the contour of the face and blurred shapes as opposed to high SF representing fine-grained information such as eyelashes or fine wrinkles). This pattern was modulated by implicit race biases: higher implicit biases are associated with a significantly higher reliance on low SF with White than with Black faces. Copyright: © 2020 Charbonneau et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.},
note = {Publisher: Public Library of Science},
keywords = {adult, African American, African Americans, article, Attitude, Caucasian, decision making, Ethics, European Continental Ancestry Group, extraction, eyelash, Facial Expression, facies, female, human, Humans, Judgment, male, perception, physiology, psychology, Racism, Social Perception, Stereotyping, visual information, wrinkle, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Black people are still considered to be one of the most stigmatized groups and have to face multiple prejudices that undermine their well-being. Assumptions and beliefs about other racial groups are quite pervasive and have been shown to impact basic social tasks such as face processing. For example, individuals with high racial prejudice conceptualize other-race faces as less trustworthy and more criminal. However, it is unknown if implicit racial bias could modulate even low-level perceptual mechanisms such as spatial frequency (SF) extraction when judging the level of trustworthiness of other-race faces. The present study showed that although similar facial features are used to judge the trustworthiness of White and Black faces, own-race faces are processed in lower SF (i.e. coarse information such as the contour of the face and blurred shapes as opposed to high SF representing fine-grained information such as eyelashes or fine wrinkles). This pattern was modulated by implicit race biases: higher implicit biases are associated with a significantly higher reliance on low SF with White than with Black faces. Copyright: © 2020 Charbonneau et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
4.
Royer, J.; Blais, C.; Charbonneau, I.; Déry, K.; Tardif, J.; Duchaine, B.; Gosselin, F.; Fiset, D.
Greater reliance on the eye region predicts better face recognition ability Journal Article
In: Cognition, vol. 181, pp. 12–20, 2018, ISSN: 00100277, (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Adolescent, adult, article, clinical article, extraction, Eye, Facial Recognition, female, human, human experiment, Humans, male, recognition, Recognition (Psychology), visual information, Young Adult
@article{royer_greater_2018,
title = {Greater reliance on the eye region predicts better face recognition ability},
author = {J. Royer and C. Blais and I. Charbonneau and K. Déry and J. Tardif and B. Duchaine and F. Gosselin and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051252949&doi=10.1016%2fj.cognition.2018.08.004&partnerID=40&md5=e1af5e939ec7381c82ff3d13d1c3cc51},
doi = {10.1016/j.cognition.2018.08.004},
issn = {00100277},
year = {2018},
date = {2018-01-01},
journal = {Cognition},
volume = {181},
pages = {12–20},
abstract = {Interest in using individual differences in face recognition ability to better understand the perceptual and cognitive mechanisms supporting face processing has grown substantially in recent years. The goal of this study was to determine how varying levels of face recognition ability are linked to changes in visual information extraction strategies in an identity recognition task. To address this question, fifty participants completed six tasks measuring face and object processing abilities. Using the Bubbles method (Gosselin & Schyns, 2001), we also measured each individual's use of visual information in face recognition. At the group level, our results replicate previous findings demonstrating the importance of the eye region for face identification. More importantly, we show that face processing ability is related to a systematic increase in the use of the eye area, especially the left eye from the observer's perspective. Indeed, our results suggest that the use of this region accounts for approximately 20% of the variance in face processing ability. These results support the idea that individual differences in face processing are at least partially related to the perceptual extraction strategy used during face identification. © 2018 Elsevier B.V.},
note = {Publisher: Elsevier B.V.},
keywords = {Adolescent, adult, article, clinical article, extraction, Eye, Facial Recognition, female, human, human experiment, Humans, male, recognition, Recognition (Psychology), visual information, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Interest in using individual differences in face recognition ability to better understand the perceptual and cognitive mechanisms supporting face processing has grown substantially in recent years. The goal of this study was to determine how varying levels of face recognition ability are linked to changes in visual information extraction strategies in an identity recognition task. To address this question, fifty participants completed six tasks measuring face and object processing abilities. Using the Bubbles method (Gosselin & Schyns, 2001), we also measured each individual's use of visual information in face recognition. At the group level, our results replicate previous findings demonstrating the importance of the eye region for face identification. More importantly, we show that face processing ability is related to a systematic increase in the use of the eye area, especially the left eye from the observer's perspective. Indeed, our results suggest that the use of this region accounts for approximately 20% of the variance in face processing ability. These results support the idea that individual differences in face processing are at least partially related to the perceptual extraction strategy used during face identification. © 2018 Elsevier B.V.