Other publications by Jean-Christophe Nebel
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J. Martinez del Rincon, M. Lewandowski, J.-C. Nebel and D. Makris
IEEE Transactions on Cybernetics
44(9): 1646-1660, 2014
[PDF]
Abstract
This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.
Cited by ( Google Scholar: 20, ISI Web of Knowledge: 13 & SCOPUS: 12 ): 22
2024