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)
—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)
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)
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)
This article addresses the problem of near real time video analysis of a maritime scene using a (moving) airborne RGB video camera in the goal of detecting and eventually recognizing a target maritime vessel. This is a very challenging problem mainly due to the high level of uncertainty of a maritime scene including a dynamic and noisy background, camera's(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)
In this paper, an efficient unsupervised approach for extracting objects from maritime background using solely still video images is proposed. Its main idea is that maritime background (sea) has the main particularity of absorbing only hot light frequencies (red and green), while an object has not this property. Therefore if a timely vector of class(More)