Antoine Lejeune

A new jump edge detection method for 3D cameras

Edges are 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 Cannys 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.

I-see-3D ! An interactive and immersive system that dynamically adapts 2D projections to the location of a user's eyes

This paper presents a non-intrusive 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 I-see-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 in such a way that the deformation of the image projected on the floor is taken into account, as well as the position of the users “eye” in its virtual world. The resulting projection is neither perspective nor orthographic. Nevertheless, we describe how this can 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.

A probabilistic pixel-based approach to detect humans in video streams

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.

A physically motivated pixel-based model for background subtraction in 3D images

This paper proposes a new pixel-based background subtraction technique, applicable to range images, to detect motion. Our method exploits the physical meaning of depth information, which leads to an improved background/foreground segmentation and the instantaneous suppression of ghosts that would appear on color images. In particular, our technique considers certain characteristics of depth measurements, such as failures for certain pixels or the non-uniformity of the spatial distribution of noise in range images, to build an improved pixel-based background model. Experiments show that incorporating specificities related to depth measurements allows us to propose a method whose performance is increased with respect to other state-of-the-art methods.

Probabilistic Framework for the Characterization of Surfaces and Edges in Range Images, with Application to Edge Detection

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images. 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.

Boosting shape classifiers accuracy by considering the inverse shape

Many techniques exist for describing shapes. These techniques almost exclusively consider the contour or the inside of the shape; the major problem for describing the outside of a shape, or inverse shape, being that it has an infinite extension. In this paper, we show how to adapt two shape descriptors , one region based, the Cover By Rectangles, and one transform based, the Zernike moments, to be applicable to the inverse shape. We analyze their properties, and show how to deal with the infinite extension of the inverse shape. Then, we apply these descriptors to shape classification and compare representations that use the shape, its inverse, or both. Our experiments establish that, for shape classification, a representation integrating the inverse shape often outperforms a representation restricted to the shape. This opens the path for better techniques that could use, as a rule of thumb, both the representations of a shape and its inverse for the purpose of classification.

Evaluation of pairwise calibration techniques for range cameras and their ability to detect a misalignment

Many applications require the use of multiple cameras to cover a large volume. In this paper, we evaluate several pairwise calibration techniques dedicated to multiple range cameras. We compare the precision of a self-calibration technique based on the movement in front of the cameras to object based calibration. While the self-calibration technique is less precise than its counterparts, it yields a first estimation of the transformation between the cameras and permits to detect when the cameras become mis-aligned. Therefore, this technique is useful in a practical situations.