Publications of Antoine Lejeune
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See detailOn-the-fly domain adaptation of binary classifiers
Pierard, Sébastien ULg; Marcos Alvarez, Alejandro ULg; Lejeune, Antoine ULg et al

in 23rd Belgian-Dutch Conference on Machine Learning (BENELEARN) (2014, June 06)

This work considers the on-the-fly domain adaptation of supervised binary classifiers, learned off-line, in order to adapt them to a target context. The probability density functions associated to ... [more ▼]

This work considers the on-the-fly domain adaptation of supervised binary classifiers, learned off-line, in order to adapt them to a target context. The probability density functions associated to negative and positive classes are supposed to be mixtures of the source distributions. Moreover, the mixture weights and the priors are only available at runtime. We present a theoretical solution to this problem, and demonstrate the effectiveness of the proposed approach on a real computer vision application. Our theoretical solution is applicable to any classifier approximating Bayes' classifier. [less ▲]

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See detailI-see-3D! An Interactive and Immersive System that dynamically adapts 2D projections to the location of a user’s eyes
Pierard, Sébastien ULg; Pierlot, Vincent ULg; Lejeune, Antoine ULg et al

in International Conference on 3D Imaging (IC3D) (2012, December)

This paper presents a system that gives the illusion of a 3D immersive and interactive environment with 2D projectors. The user does not need to wear glasses, nor to watch a (limited) screen. The virtual ... [more ▼]

This paper presents a system that gives the illusion of a 3D immersive and interactive environment with 2D projectors. The user does not need to wear glasses, nor to watch a (limited) screen. The virtual world is all around him, drawn on the floor. As the user is himself immersed in the virtual world, there is no need for a proxy like an avatar; he can move inside the virtual environment freely. Moreover, the Isee-3D system allows a user to manipulate virtual objects with his own body, making interactions with the virtual world very intuitive. Giving the illusion of 3D requires to render images insuch a way that the deformation of the image projected on thefloor is taken into account, as well as the position of the user’s “eye” in its virtual world. The resulting projection is neither perspective nor orthographic. Nevertheless, we describe how thiscan be implemented with the standard OpenGL pipeline, without any shader. Our experiments demonstrate that our system is effective in giving the illusion of 3D. Videos showing the results obtained with our I-see-3D system are available on our website http://www.ulg.ac.be/telecom/projector [less ▲]

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See detailAdaptive bilateral filtering for range images
Lejeune, Antoine ULg; Van Droogenbroeck, Marc ULg; Verly, Jacques ULg

in URSI Benelux Forum 2012 (2012, September 14)

We propose an improvement for range images of the standard bilateral filter consisting in adapting it locally to the distribution of the range noise at each pixel.

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See detailUtilisation de la Kinect
Lejeune, Antoine ULg; Pierard, Sébastien ULg; Van Droogenbroeck, Marc ULg et al

in Linux Magazine France (2012), 151

Fin 2010, Microsoft lançait la Kinect pour Xbox 360, la première caméra 3D destinée au grand public. Une semaine plus tard sortait la première librairie permettant d'utiliser l'appareil sur un ordinateur ... [more ▼]

Fin 2010, Microsoft lançait la Kinect pour Xbox 360, la première caméra 3D destinée au grand public. Une semaine plus tard sortait la première librairie permettant d'utiliser l'appareil sur un ordinateur personnel. Depuis lors, des centaines d'applications ont vu le jour utilisant l'information de profondeur capturée par la Kinect pour analyser le mouvement humain ou guider des robots. Dans cette article, nous allons voir comment développer une application utilisant la Kinect sous GNU/Linux. [less ▲]

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See detailRecognition of emotions in facial images
Pierard, Sébastien ULg; Lejeune, Antoine ULg; Van Droogenbroeck, Marc ULg

Report (2012)

This document reports about some work done in the field of emotions classification in facial images.

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See detailThe secrets of the Kinect ... in depth!
Lejeune, Antoine ULg; Van Droogenbroeck, Marc ULg; Verly, Jacques ULg

Conference (2011, December 08)

The slides present the Kinect in depth. The major technological ideas are explained. In addition, the presentation gives an overview of all the libraries available to interface to the Kinect.

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See detailA new jump edge detection method for 3D cameras
Lejeune, Antoine ULg; Pierard, Sébastien ULg; Van Droogenbroeck, Marc ULg et al

in International Conference on 3D Imaging (IC3D) (2011, December)

Edges is a fundamental clue for analyzing, interpreting, and understanding 3D scenes: they describe objects boundaries. Available edge detection methods are not suited for 3D cameras such as the Microsoft ... [more ▼]

Edges is a fundamental clue for analyzing, interpreting, and understanding 3D scenes: they describe objects boundaries. Available edge detection methods are not suited for 3D cameras such as the Microsoft Kinect or a time-of-flight camera: they are slow and do not take into consideration the characteristics of the cameras. In this paper, we present a fast jump edge detection technique for 3D cameras based on the principles of Canny’s edge detector. We first analyze the characteristics of the range signal for two different kinds of cameras: a time-of-flight camera (the PMD[vision] CamCube) and the Microsoft Kinect. From this analysis, we define appropriate operators and thresholds to perform the edge detection. Then, we present some results of the developed algorithms for both cameras. [less ▲]

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See detailA probabilistic pixel-based approach to detect humans in video streams
Pierard, Sébastien ULg; Lejeune, Antoine ULg; Van Droogenbroeck, Marc ULg

in International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011) (2011, May)

Human detection in video streams is an important task in many applications including video surveillance. Surprisingly, only few papers have been devoted to this topic. This paper presents a new approach ... [more ▼]

Human detection in video streams is an important task in many applications including video surveillance. Surprisingly, only few papers have been devoted to this topic. This paper presents a new approach to detect humans in video streams. Our approach is based on the temporal information present in videos. A background subtraction algorithm is first used to segment the silhouettes of the users and the moving objects. Then a classification process in two steps determines for each connected component if it corresponds to the silhouette of a human or not. During the first step, a probabilistic information is computed for each pixel independently. The information from a subset of pixels is then gathered to predict the class of the observed silhouette. This paper presents the principles and some results obtained on real silhouettes. It is shown that our approach is efficient for the detection of humans in video streams. [less ▲]

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See detail3D information is valuable for the detection of humans in video streams
Pierard, Sébastien ULg; Lejeune, Antoine ULg; Van Droogenbroeck, Marc ULg

in 3D Stereo Media (2010, December)

In this paper, we propose a technique based on 3D information (also called depth or range) for the detection of humans. First, a background subtraction technique operates to detect the silhouettes of ... [more ▼]

In this paper, we propose a technique based on 3D information (also called depth or range) for the detection of humans. First, a background subtraction technique operates to detect the silhouettes of humans and objects moving in the scene. Then, a machine learning algorithm is used to predict if a silhouette annotated with depth matches a human silhouette or not. The complete method is designed to cope with defects introduced during the segmentation step. Results, obtained on computer generated data, show that 3D depth data is a valuable information for detecting humans in that it improves over techniques based on binary silhouettes. In our experiments, we have reached an accuracy of 99.9% thanks to the depth information. [less ▲]

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