This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unknown correspondences are handled via mixture models. Adopting a maximum likelihood principle, we introduce an innovative EM-like algorithm, namely, the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm. The algorithm allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case. We analyze in detail the associated consequences in terms of estimation of the registration parameters, and propose an optimal method for estimating the rotational and translational parameters based on semidefinite positive relaxation. We extend rigid registration to articulated registration. Robustness is ensured by detecting and rejecting outliers through the addition of a uniform component to the Gaussian mixture model at hand. We provide an in-depth analysis of our method and compare it both theoretically and experimentally with other robust methods for point registration.

After the seminal work by Taqqu relating self-similarity to heavy-tailed distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like Web file sizes and flow lengths, were heavy-tailed. However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated using either numerical or network simulations, or from uncontrolled Web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarios, prescribing specific heavy-tailed distributions, and estimating confidence intervals. With this goal in mind, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, combined with state-of-the-art estimators, to investigate the prediction's observability on real networks. To this end, we organize a large number of controlled traffic circulation sessions on a nationwide real network involving 200 independent hosts. We use a FPGA-based measurement system to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow-size distributions, independently. Not only do our results complement and validate, with a striking accuracy, some conclusions drawn from a series of pioneering studies, but they also bring in new insights on the controversial role of certain components of real networks.

This contribution aims at performing a longitudinal study of the evolution of the traffic collected every day for seven years on a trans-Pacific backbone link (the MAWI dataset). Long term characteristics are investigated both at TCP/IP layers (packet and flow attributes) and application usages. The analysis of this unique dataset provides new insights into changes in traffic statistics, notably on the persistence of Long Range Dependence, induced by the on-going increase in link bandwidth. Traffic in the MAWI dataset is subject to bandwidth changes, to congestions, and to a variety of anomalies. This allows the comparison of their impacts on the traffic statistics but at the same time significantly impairs long term evolution characterizations. To account for this difficulty, we show and explain how and why random projection (sketch) based analysis procedures provide practitioners with an efficient and robust tool to disentangle actual long term evolutions from time localized events such as anomalies and link congestions. Our central results consist in showing a strong and persistent long range dependence controlling jointly byte and packet counts. An additional study of a 24-hour trace complements the long-term results with the analysis of intraday variabilities.

We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of an articulated object, as well as probabilities that the data are assigned either to an object part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes.

Making virtual modeling as easy and intuitive as real-clay manipulation is still an unsolved problem. This paper takes a step in this direction: in addition to offering standard features such as addition and removal of material, it uses a new real-time plasticity model to let the user apply local and global deformations such as those made in real clay by pressing with a finger or bending a sculpture with one hands. Although not completely physically accurate, the model exhibits several important features of real clay, namely plasticity, mass conservation, and surface tension effects. These features make the model intuitive, since the user obtains the shapes he expects, as demonstrated by our results.