244 research outputs found
Real-time lumen detection for autonomous colonoscopy
Full text linkLumen detection and tracking in the large bowel is a key prerequisite step for autonomous navigation of endorobots for colonoscopy. Attempts at detecting and tracking the lumen so far have been made using optical flow and shape-from-shading techniques. In general, these methods are computationally expensive, and most are either not real-time nor tested on real devices. To this end, we present a deep learning-based approach for lumen localisation from colonoscopy videos. We avoid the need for extensive, costly annotations with a semi-supervised learning and a self-training scheme, whereby only a small subset of video frames is annotated. We develop an end-to-end pseudo-labelling semi-supervised approach incorporating a self-training scheme for colon lumen detection. Our approach reveals a competitive performance to the supervised baseline model with both objective and subjective evaluation metrics, while saving heavy labelling costs in terms of clinicians’ time. Our method for lumen detection runs at 60 ms per frame during the inference phase. Our experiments demonstrate the potential of our system in real-time environments, which contributes towards improving the automation of robotics colonoscopy
Calcul de pose dynamique avec les caméras CMOS utilisant une acquisition séquentielle
Full text linkComputer Vision, a field of Computer Science, is about extracting information from cameras. Their sensors can be produced using the CMOS technology which is widely used on mobile devices due to its low cost and volume. This technology allows a fast acquisition of an image by sequentially exposin the scan-line. However this method produces some deformation in the image if there is a motion between the camera and the filmed scene. This effect is known as Rolling Shutter and various methods have tried to remove these artifacts. Instead of correcting it, previous works have shown methods to extract information on the motion from this effect. These methods rely on a extension of the usual geometrical model of cameras by taking into account the sequential acquisition and the motion, supposed uniform, between the sensor and the scene. From this model, it’s possible to extend the usual pose estimation (estimation of position and orientation of the camera in the scene) to also estimate the motion parameters. Following on from this approach, we will present an extension to non-uniform motions based on a smoothing of the derivatives of the motion parameters. Afterwards, we will present a polynomial model of the Rolling Shutter and a global optimisation method to estimate the motion parameters. Well implemented, this enables to establish an automatic matching between the 3D model and the image. We will conclude with a comparison of all these methods using either simulated or real data.En informatique, la vision par ordinateur s’attache à extraire de l’information à partir de caméras. Les capteurs de celles-ci peuvent être produits avec la technologie CMOS que nous retrouvons dans les appareils mobiles en raison de son faible coût et d’un encombrement réduit. Cette technologie permet d’acquérir rapidement l’image en exposant les lignes de l’image de manière séquentielle. Cependant cette méthode produit des déformations dans l’image s’il existe un mouvement entre la caméra et la scène filmée. Cet effet est connu sous le nom de «Rolling Shutter» et de nombreuses méthodes ont tenté de corriger ces artefacts. Plutôt que de le corriger, des travaux antérieurs ont développé des méthodes pour extraire de l’information sur le mouvement à partir de cet effet. Ces méthodes reposent sur une extension de la modélisation géométrique classique des caméras pour prendre en compte l’acquisition séquentielle et le mouvement entre le capteur et la scène, considéré uniforme. À partir de cette modélisation, il est possible d’étendre le calcul de pose habituel (estimation de la position et de l’orientation de la scène par rapport au capteur) pour estimer aussi les paramètres du mouvement. Dans la continuité de cette démarche, nous présenterons une généralisation à des mouvements non-uniformes basée sur un lissage des dérivées des paramètres de mouvement. Ensuite nous présenterons une modélisation polynomiale du «Rolling Shutter» et une méthode d’optimisation globale pour l’estimation de ces paramètres. Correctement implémenté, cela permet de réaliser une mise en correspondance automatique entre le modèle tridimensionnel et l’image. Pour terminer nous comparerons ces différentes méthodes tant sur des données simulées que sur des données réelles et conclurons
Dynamic pose estimation with CMOS cameras using sequential acquisition
No full textEn informatique, la vision par ordinateur s’attache à extraire de l’information à partir de caméras. Les capteurs de celles-ci peuvent être produits avec la technologie CMOS que nous retrouvons dans les appareils mobiles en raison de son faible coût et d’un encombrement réduit. Cette technologie permet d’acquérir rapidement l’image en exposant les lignes de l’image de manière séquentielle. Cependant cette méthode produit des déformations dans l’image s’il existe un mouvement entre la caméra et la scène filmée. Cet effet est connu sous le nom de «Rolling Shutter» et de nombreuses méthodes ont tenté de corriger ces artefacts. Plutôt que de le corriger, des travaux antérieurs ont développé des méthodes pour extraire de l’information sur le mouvement à partir de cet effet. Ces méthodes reposent sur une extension de la modélisation géométrique classique des caméras pour prendre en compte l’acquisition séquentielle et le mouvement entre le capteur et la scène, considéré uniforme. À partir de cette modélisation, il est possible d’étendre le calcul de pose habituel (estimation de la position et de l’orientation de la scène par rapport au capteur) pour estimer aussi les paramètres du mouvement. Dans la continuité de cette démarche, nous présenterons une généralisation à des mouvements non-uniformes basée sur un lissage des dérivées des paramètres de mouvement. Ensuite nous présenterons une modélisation polynomiale du «Rolling Shutter» et une méthode d’optimisation globale pour l’estimation de ces paramètres. Correctement implémenté, cela permet de réaliser une mise en correspondance automatique entre le modèle tridimensionnel et l’image. Pour terminer nous comparerons ces différentes méthodes tant sur des données simulées que sur des données réelles et conclurons.Computer Vision, a field of Computer Science, is about extracting information from cameras. Their sensors can be produced using the CMOS technology which is widely used on mobile devices due to its low cost and volume. This technology allows a fast acquisition of an image by sequentially exposin the scan-line. However this method produces some deformation in the image if there is a motion between the camera and the filmed scene. This effect is known as Rolling Shutter and various methods have tried to remove these artifacts. Instead of correcting it, previous works have shown methods to extract information on the motion from this effect. These methods rely on a extension of the usual geometrical model of cameras by taking into account the sequential acquisition and the motion, supposed uniform, between the sensor and the scene. From this model, it’s possible to extend the usual pose estimation (estimation of position and orientation of the camera in the scene) to also estimate the motion parameters. Following on from this approach, we will present an extension to non-uniform motions based on a smoothing of the derivatives of the motion parameters. Afterwards, we will present a polynomial model of the Rolling Shutter and a global optimisation method to estimate the motion parameters. Well implemented, this enables to establish an automatic matching between the 3D model and the image. We will conclude with a comparison of all these methods using either simulated or real data
Data of Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites
No full textData related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data)
title = "Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites",
journal = "European Journal of Mechanics - A/Solids",
year = "2022",
volume = "91",
pages = " 104384 ",
doi = "https://doi.org/10.1016/j.euromechsol.2021.104384",
author = "Nguyen, Van Dung and Noels, Ludovic"Van Dung Nguyen is a Postdoctoral Researcher at the Belgian National Fund for Scientific Research (FNRS
Ludovic-Mohamed Zahed’s Universal Performance of French Citizenship and Muslim Brotherhood
No full textIn this chapter, I present the life and work of Ludovic-Mohamed Zahed, who is the founder of three non-profit associations over the past several years: Les Enfants du Sida (2006), Homosexuels musulmans de France (HM2F) (2010), and Musulman-es Progressistes de France (2012). He is also the author of Révoltes extraordinaires: un enfant du sida autour du monde (2011) and Le Coran et La Chair (2012), and co-author of Queer Muslim Marriage (2013). During the last few years, the French media have covered his same-sex marriage in Cape Town to husband Qiyaam Jantjies-Zahed in 2011, the publication of his book, Le Coran et La Chair in 2012, as well as and his creation of La Mosquée inclusive de l’Unicité, the first “gay friendly” or inclusive mosque in Paris, in 2012.</p
Practical Projective Structure from Motion (P<sup>2</sup>SfM)
No full textThis paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear Least Squares systems instead of homogeneous ones. In order to do so, we show that an extension of the linear constraints from the Generalized Projective Reconstruction Theorem can be transferred to the projective parameters, ensuring also a valid projective reconstruction in the process. We use an incremental approach that, starting from a solvable sub-problem, incrementally adds views and points until completion with a robust, outliers free, procedure. Experiments with simulated data shows that our approach is performing well, both in term of the quality of the reconstruction and the capacity to handle missing data and outliers with a reduced computational time. Finally, results on real datasets shows the ability of the method to be used in medium and large scale 3D reconstruction scenarios with high ratios of missing data (up to 98%).</p
Verification Failures: Assessing the Sample Quality of Fingerprints collected in an African Election Setting
No full textThe use of biometric technology has become an integral part of elections in Africa, the primary aim being delivery of credible elections. Fingerprint verification of eligible voters is central to this development. Deployment of fingerprint verification technology at elections has not been without its challenges for African countries. Failed verification incidents have been recorded in countries like Ghana, Kenya and Nigeria. A case is made on the need to identify the causes of these incidents before any reasonable solution can be proposed. This research investigates some of the possible causes by analysing the quality of sample fingerprints from a new dataset of an African population collected in election settings. NIST’s NFIQ 2.2 was used for the fingerprint quality assessment with initial analyses reported in this work
Revisiting Projective Structure from Motion:A Robust and Efficient Incremental Solution
No full textThis paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear Least Squares systems instead of homogeneous ones. In order to do so, we show that an extension of the linear constraints from the Generalized Projective Reconstruction Theorem can be transferred to the projective parameters, ensuring also a valid projective reconstruction in the process. We use an incremental approach that, starting from a solvable sub-problem, incrementally adds views and points until completion with a robust, outliers free, procedure. To prevent error accumulation, a refinement based on alternation between new estimations of views and points is used. This can also be done with constrained non-linear optimization. Experiments with simulated data shows that our approach is performing well, both in term of the quality of the reconstruction and the capacity to handle missing data and outliers with a reduced computational time. Finally, results on real datasets shows the ability of the method to be used in medium and large scale 3D reconstruction scenarios with high ratios of missing data (up to 98 percent).</p
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