Simon Mure

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A new spatio-temporal filtering scheme based on the mean-shift procedure, which computes unsupervised spatio-temporal filtering for univariate feature evolution, is proposed in this paper. Our main contributions are on one hand themodification of the spatial/range domains to appropriately integrate the temporal feature into the mean-shift iterative form and(More)
In this paper, we propose a mean-shift formulation allowing spatiotemporal clustering of video streams, and possibly extensible to other multivariate evolving data. Our formulation enables causal or omniscient filtering of spatiotemporal data, which is robust to total object occlusions. It embeds a new clustering algorithm within the filtering procedure(More)
Based on weekly up to monthly follow-up of MS patients over one year with T2-weighted magnetic resonance images, a new clustering scheme is proposed to automatically identify lesions sharing similar temporal behaviors. The proposed method, based on spatiotemporal mean-shift and dynamic time warping, allows to detect intra and inter-patient similarities in(More)
Most time-series clustering methods, such as k-means or k-medoids, are initialized by prior knowledge about the number of classes or by a learning step. We propose an unsupervised clustering technique based on spatiotemporal mean-shift and optimal time series warping using dynamic time warping (DTW). Our main contribution consists in combining a(More)
In this paper, we propose a new anisotropic diffusion formulation allowing non-linear spatiotemporal filtering of image sequences. We first formulate a multidimensional spatiotemporal diffusion equation based on Barash's iterative form, processing independently both spatial, temporal and intensity dimensions with their own diffusion functions and scale(More)
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