References of "Verly, Jacques"
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See detailStereopsia, the World Immersion Forum
Verly, Jacques ULiege

Scientific conference (2018, September 04)

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See detailSome novel applications of VR in the domain of health
Grogna, David ULiege; Stassart, Céline ULiege; Servotte, Jean-Christophe ULiege et al

Scientific conference (2018, September 04)

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See detailTrends for immersion technologies and content
Verly, Jacques ULiege; Grogna, David ULiege

Scientific conference (2018, September 04)

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See detailProbabilistic Framework for the Characterization of Surfaces and Edges in Range Images, with Application to Edge Detection
Lejeune, Antoine ULiege; Verly, Jacques ULiege; Van Droogenbroeck, Marc ULiege

in IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), 40(9), 2209-2222

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge ... [more ▼]

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge detection, feature extraction, and classification. We use the geometrical nature of the data to derive an analytic expression for the joint probability density function (pdf) for the random variables used to model the ranges of a set of pixels in a local neighborhood of an image. We decompose this joint pdf by considering independently the cases where two real world points corresponding to two neighboring pixels are locally on the same real world surface or not. In particular, we show that this joint pdf is linked to the Voigt pdf and not to the Gaussian pdf as it is assumed in some applications. We apply our framework to edge detection and develop a locally adaptive algorithm that is based on a probabilistic decision rule. We show in an objective evaluation that this new edge detector performs better than prior art edge detectors. This proves the benefits of the probabilistic characterization of the local neighborhood as a tool to improve applications that involve range images. [less ▲]

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See detailMulti-timescale drowsiness characterization based on a video of a driver’s face
Massoz, Quentin ULiege; Verly, Jacques ULiege; Van Droogenbroeck, Marc ULiege

in Sensors (2018), 18(9), 2801

Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and ... [more ▼]

Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and timely warnings of drowsiness to the driver. In practice, the least intrusive, physiology-based approach is to remotely monitor, via cameras, facial expressions indicative of drowsiness such as slow and long eye closures. Since the system’s decisions are based upon facial expressions in a given time window, there exists a trade-off between accuracy (best achieved with long windows, i.e., at long timescales) and responsiveness (best achieved with short windows, i.e., at short timescales). To deal with this trade-off, we develop a multi-timescale drowsiness characterization system composed of four binary drowsiness classifiers operating at four distinct timescales (5 s, 15 s, 30 s, and 60 s) and trained jointly. We introduce a multi-timescale ground truth of drowsiness, based on the reaction times (RTs) performed during standard Psychomotor Vigilance Tasks (PVTs), that strategically enables our system to characterize drowsiness with diverse trade-offs between accuracy and responsiveness. We evaluated our system on 29 subjects via leave-one-subject-out cross-validation and obtained strong results, i.e., global accuracies of 70%, 85%, 89%, and 94% for the four classifiers operating at increasing timescales, respectively. [less ▲]

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See detailSome novel applications of VR in the domain of health
Grogna, David ULiege; Stassart, Céline ULiege; Servotte, Jean-Christophe ULiege et al

in Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) (2018, August)

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See detailTrends for immersion technologies and content
Verly, Jacques ULiege; Grogna, David ULiege

in Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) (2018, August)

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See detailExcellent Potential of Geometric Brownian Motion (GBM) as a Random Process Model for Level of Drowsiness Signals
Ebrahimbabaie Varnosfaderani, Pouyan ULiege; Verly, Jacques ULiege

in Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (2018, January 20)

We show that Geometric Brownian Motion (GBM) appears to be an excellent choice of random process model to describe mathematically the real-life signals that represent the evolution with time of the level ... [more ▼]

We show that Geometric Brownian Motion (GBM) appears to be an excellent choice of random process model to describe mathematically the real-life signals that represent the evolution with time of the level of drowsiness (LoD) of an individual, such as a driver. We collected data from thirty (30) healthy participants, who each underwent three tests (either driving in a simulator or performing Psychomotor Vigilance Tests) at successive levels of sleep deprivation. During each test, the LoD was produced by a photooculography (POG) based device designed and built by our team. We so obtained a total of 90 LoD signals. For each, we applied statistical methods to determine whether a GBM was a valid model for it. All 90 signals passed statistical tests of normality and independency, meaning that each can be modeled by GBM, thereby showing the excellent potential of GBM as a random process model for LoD signals. This finding could lead to the development of a number of innovative means for predicting the evolution of the LoD and the occurrence of related events beyond the present moment. The resulting technology should help reduce the number of accidents due to drowsy driving. [less ▲]

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See detailValidated assessment of gait sub-phase durations in older adults using an accelerometer-based ambulatory system
Boutaayamou, Mohamed ULiege; GILLAIN, Sophie ULiege; Schwartz, Cédric ULiege et al

in Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) (2018)

Validated extraction of gait sub-phase durations using an ambulatory accelerometer-based system is a current unmet need to quantify subtle changes during the walking of older adults. In this paper, we ... [more ▼]

Validated extraction of gait sub-phase durations using an ambulatory accelerometer-based system is a current unmet need to quantify subtle changes during the walking of older adults. In this paper, we describe (1) a signal processing algorithm to automatically extract not only durations of stride, stance, swing, and double support phases, but also durations of sub-phases that refine the stance and swing phases from foot-worn accelerometer signals in comfortable walking of older adults, and (2) the validation of this extraction using reference data provided by a gold standard system. The results show that we achieve a high agreement between our method and the reference method in the extraction of (1) the temporal gait events involved in the estimation of the phase/sub-phase durations, namely heel strike (HS), toe strike (TS), toe-off (TO), maximum of heel clearance (MHC), and maximum of toe clearance (MTC), with an accuracy and precision that range from ‒3.6 ms to 4.0 ms, and 6.5 ms to 12.0 ms, respectively, and (2) the gait phase/sub-phase durations, namely stride, stance, swing, double support phases, and HS to TS, TO to MHC, MHC to MTC, and MTC to HS sub-phases, with an accuracy and precision that range from ‒4 ms to 5 ms, and 9 ms to 15 ms, respectively, in comfortable walking of a thirty-eight older adults ( (mean ± standard deviation) 71.0 ± 4.1 years old). This demonstrates that the developed accelerometer-based algorithm can extract validated temporal gait events and phase/sub-phase durations, in comfortable walking of older adults, with a promising degree of accuracy/precision compared to reference data, warranting further studies. [less ▲]

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See detailSome novel applications of virtual reality (VR) in the domain of health
Grogna, David ULiege; Stassart, Céline ULiege; Bragard, Isabelle ULiege et al

Conference (2017, December 12)

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See detailA gait cycle partitioning method using a foot-worn accelerometer system
Boutaayamou, Mohamed ULiege; Bruls, Olivier ULiege; Denoël, Vincent ULiege et al

Conference (2017, November 30)

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See detailA gait cycle partitioning method using a foot-worn accelerometer system
Boutaayamou, Mohamed ULiege; Bruls, Olivier ULiege; Denoël, Vincent ULiege et al

Conference (2017, November 30)

Detailed reference viewed: 18 (3 ULiège)