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Tracking Articulated Bodies using Generalized Expectation Maximization

Publications associées (51)

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Mahalanobis Motion Generation

Daniel Thalmann, Pascal Fua, Raquel Urtasun, Pascal Glardon

Representing motions as linear sums of principal components has become a widely accepted animation technique. While powerful, the simplest version of this approach is not particularly well suited to modeling the specific style of an individual whose motion ...

2004

Tuning of a Basic Coordination Pattern Constructs Straight-Ahead and Curved Walking in Humans

Grégoire Courtine

We tested the hypothesis that common principles govern the production of the locomotor patterns for both straight-ahead and curved walking. Whole body movement recordings showed that continuous curved walking implies substantial, limb-specific changes in n ...

2004

PCA-based walking engine using motion capture data

Daniel Thalmann, Ronan Boulic, Pascal Glardon

This paper aims to propose a novel approach to generate new generic human walking patterns using motion-captured data, leading to a real-time engine intended for virtual humans animation. The method applies the PCA (principal component analysis) technique ...

2004

Variational Information Maximization in Gaussian Channels

Recently, we introduced a simple variational bound on mutual information, that resolves some of the difficulties in the application of information theory to machine learning. Here we study a specific application to Gaussian channels. It is well known that ...

IDIAP2004

Independent Component Analysis and Support Vector Machine for Face Feature Extraction

Jean-Philippe Thiran, Gianluca Antonini, Vlad Popovici

We propose Independent Component Analysis representation and Support Vector Machine classification to extract facial features in a face detection/localization context. The goal is to find a better space where project the data in order to build ten differen ...

IEEE2003

PCA in Autocorrelation Space

Jean-Philippe Thiran, Vlad Popovici

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing ...

SPIE2002

Transforming the feature vectors to improve HMM based cursive word recognition systems

Samy Bengio, Alessandro Vinciarelli

Although many Offline Cursive Word Recognition systems are based on HMMs, no attention was ever paid, to our knowledge, to the fact that the feature vectors are typically not in the most suitable form for modeling. They are most of the time correlated and ...

IDIAP2002

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000