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We address the problem of decomposing a sequence of spectroscopic signals. Data are a series of signals modeled as a noisy sum of parametric peaks. We aim to estimate the peak parameters given that they change slowly between two contiguous signals. The key idea is to decompose the whole sequence rather than each signal independently. The problem is set within a Bayesian framework. The peaks with similar evolution are gathered into groups and a Markovian prior on the peak parameters of a same group is used to favor a smooth evolution of the peaks. In addition, the peak number and the group number are unknown and have to be estimated (the number of peaks in two contiguous signals change if peaks vanish). Therefore, the posterior distribution is sampled with a reversible jump Markov chain Monte Carlo algorithm.
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Chloé Mercier,Sylvain Faisan,Alexandre Pron,Nadine Girard,Guillaume Auzias, Thierry Chonavel,François Rousseau
2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA)pp.1-6, (2023)
Pattern Recognition Letters (2023): 172-180
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Claire Terzulli,Chloe Chauvin, Cedric Champagnol Di-Liberti,Sylvain Faisan,Laurent Goffin,Coralie Gianesini,Denis Graff,Andre Dufour,Edouard Laroche,Eric Salvat,Pierrick Poisbeau
Frontiers in Pain Research (2023): 1237090-1237090
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arXiv (Cornell University) (2023)
HAL (Le Centre pour la Communication Scientifique Directe) (2022)
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Claire Terzulli,Meggane Melchior,Laurent Goffin,Sylvain Faisan, Coralie Gianesini, Denis Graff,André Dufour,Edouard Laroche, Chloé Chauvin,Pierrick Poisbeau
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