Frédéric Bouchara

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We analyze the irregularity of human postural sway data during quiet standing using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we show that the SampEn parameter is able to characterize the irregularity of the center of pressure fluctuations through the analysis of the velocity variable. We present a practical(More)
We study the effect of static additive noise on the sample entropy (SampEn) algorithm [J. S. Richman and J. R. Moorman, Am. J. Physiol. Heart Circ. Physiol. 278, 2039 (2000); R. B. Govindan et al., Physica A 376, 158 (2007)] for analyzing time series. Using surrogate data tests, we empirically investigate the ability of the SampEn index to detect(More)
We investigate human postural sway velocity time series by computing two dynamical statistics quantifying the smoothness (the central tendency measure or CTM) and the regularity (the sample entropy or SampEn) of their underlying dynamics. The purpose of the study is to investigate the effect of aging and vision on the selected measures and to explore the(More)
We present, in this paper, the FAIR algorithm: a fast algorithm for document image restoration. This algorithm has been submitted to different contests where it showed good performance in comparison to the state of the art. In addition, this method is scale invariant and fast enough to be used in real-time applications. The method is based on a(More)
—In this paper, we present a novel approach for super-resolved binarization of document images acquired by low quality devices. The algorithm tries to compute the super resolution of the likelihood of text instead of the gray value of pixels. This method is the extension of a binarization algorithm (FAIR: a Fast Algorithm for document Image Restoration)(More)
In this paper, we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group $$SE(2,N) = {\mathbb {Z}}_N\rtimes {\mathbb {R}}^2$$ S E ( 2 , N ) = Z N ⋊ R 2 . The proposed technique is based on generalized Fourier descriptors on the latter(More)