This paper presents a novel technique for 2D human motion capture using a single non calibrated camera. The user's five extremities (head, hands and feet) are extracted, labeled and tracked after silhouette segmentation. As they are the minimal number of points that can be used in order to enable whole body gestural interaction, we henceforth refer to these features as crucial points. The crucial point candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region that lie on the silhouette boundary. In order to disambiguate the selected crucial points into head, left and right foot and left and right hand classes, we propose a Bayesian framework that combines a MAP approach weighted by a prior model and the intensities of the tracked crucial points. Due to its low computational complexity, the system can run at real-time paces on standard personal computers, with an average error rate range between 2% and 7% in realistic situations, depending on the context and segmentation quality.

This paper presents a novel technique for 2D human motion estimation using a single non calibrated camera. The user's five crucial human features (head, hands and feet) are extracted, labeled and tracked, after silhouette segmentation. The crucial points candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region (silhouette) following the silhouette boundary. Selected crucial points are then classified as head, hands or feet using a probabilistic approach weighted by a prior human model. The system can run at 50 Hz paces on standard personal computers.

In order to complement subjective evaluation of the quality of segmentation masks, this paper introduces a procedure for automatically assessing this quality. Algorithmically computed figures of merit are proposed. Assuming the existence of a perfect reference mask (ground truth), generated manually or with a reliable procedure over a test set, these figures of merit take into account visually desirable properties of a segmentation mask in order to provide the user with metrics that best quantify the spatial and temporal accuracy of the segmentation masks. For the sake of easy interpretation, results are presented on a peaked signal-to-noise ratio-like logarithmic scale.

In this paper, we describe a mixed reality system based on a "magic mirror" model, in which the user's image is captured in real time by a video camera, extracted from his/her background and mixed with a 3D graphic model of a virtual image including the synthetic characters taking part in the story. The resulting image is projected on a large screen facing the user, who sees his/her own image embedded in the virtual stage with the synthetic actors. The graphic component of the mixed reality world is based on a game engine, Unreal Tournament 2003. This engine not only performs graphic rendering and character animation but incorporates a new version of our previously described storytelling engine. A single 2D camera facing the user analyses the image in real-time by segmenting the user's contours.

The paper presents a simple yet robust measure of image quality in terms of global (camera) blur. It is based on histogram computation of non-zero DCT coefficients. The technique is directly applicable to images and video frames in compressed (MPEG or JPEG) domain and to all types of MPEG frames (I-, P- or B-frames). The resulting quality measure is proved to be in concordance with subjective testing and is therefore suitable for quick qualitative characterization of images and video frames.

This paper proposes an asymmetric scheme for motion estimation/compensation. While the estimation is performed with a classical block matching algorithm, the motion information is decoded by using an active mesh in order to implement the compensation stage. A mesh is positioned by taking into account the relevant spatial information of the image to be compensated and is used afterwards to reconstruct the motion information. Two main issues have to be addressed for conducting such a motion compensation technique: (i) how to optimally design an active mesh, (ii) how to reverse and interpolate a backward motion field estimated on an a priori grid of fixed-size blocks so as to determine a forward motion field on variable size triangular patches. While proposing a solution to these two problems, particular attention is paid to the computational burden. Such a scheme opens the possibility for added manipulation functionalities because of mesh capabilities.

The tool introduced in this paper allows to automatically decide in an image or in a video sequence which regions are important and which ones are not. For this purpose, fuzzy logic has been used to modelize human subjective knowledge about the way to allocate priorities to regions. The resulting classification can be used in a wide range of applications going from image coding to image understanding.

The present paper introduces an original way to perform motion estimation and compensation. The scheme is asymmetric in the sense that the compensation stage is not merely the reverse operation of the estimation process. Motion estimation is computed using a classical block matching algorithm and the set of vectors that compose the motion information are classically transmitted to the decoder. The motion compensation uses them to deform a wireframe structure whose vertices are displaced according to the motion vectors. Conditions must be set up on the number of wireframe vertices and on their location. In comparison with a block-based compensation, the main advantage is the possibility to take into account the object information, which results in perceptually more pleasant predicted pictures.