Fabien Salzenstein

IF estimation using empirical mode decomposition and nonlinear Teager energy operator

In this paper, a method based on the empirical mode decomposition (EMD) algorithm and Teager energy operator (TEO) is proposed to estimate the instantaneous frequency (IF) of a signal embedded in noise. IF is used to describe a signals frequency that varies with time. Both EMD and TEO deal with non-stationary signals. The signal is first band pass filtered into subsignals (components) called intrinsic mode functions (IMFs) with well defined IF. Each IMF is a zero-mean AM-FM component. Then TEO tracks the modulation energy of each IMF and estimates the corresponding IF. In order to show the effectiveness of the proposed method, results of IF estimation of noisy AM-FM signals are proposed.

Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (mode of posterior marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data

Non-stationary fuzzy Markov chain

This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationary fuzzy Markov chain model is proposed in an unsupervised way, based on a recent Markov triplet approach. The method is compared with the stationary fuzzy Markovian chain model. Both stationary and non-stationary methods are enriched with a parameterized joint density, which governs the attractiveness of the neighbored states. Segmentation task is processed with Bayesian tools, such as the well known MPM (Mode of Posterior Marginals) criterion. To validate both models, we perform and compare the segmentation on synthetic images and raw optical patterns which present diffuse structures.

Unsupervised multisensor data fusion approach

A new iterative approach of multisensor data fusion based on the Dempster-Shafer (1976) formalism is presented. Mass functions, formalized by a Gaussian model, are estimated at each iteration using the output fused image and the source images. The effectiveness of the method is demonstrated on synthetic images.

Iterative estimation of Dempster-Shafer’s basic probability assignment: application to multisensor image segmentation

Basic probability assignment (BPA) definition remains a difficult problem to apply Desmpter-Shafer evidence theory to practical applications such as in image processing. A new iterative approach of multisensor data fusion based on the Dempster-Shafer framework is proposed. BPAs, modeled by a Gaussian distribution, are estimated iteratively and in an unsupervised way using the fused image and the source images. Data fusion is performed at the pixel level. Results on synthetic and real images are presented to illustrate the effectiveness of the proposed fusion scheme. Limitations of the method are discussed.

Wheezing sounds detection using multivariate generalized gaussian distributions

A wheeze is a continuous, coarse, whistling sound produced in the respiratory airways during breathing, commonly experienced by persons suffering from asthma. In this paper, we present a new method for the detection of wheezing sounds in the normal breathing sounds. In our study we perform an accurate statistical analysis of breathing signals. We suggest a modeling for wheezing and normal sounds in the wavelet packet domain using generalized gaussian distributions. Our detection method is based on a specific multimodal Markovian modeling proposed in a bayesian framework. We cope with the multidimensional aspect of the generalized gaussian distribution by using the theory of copulas. Experimental results are given in detail in this paper.

Acoustical respiratory signal analysis and phase detection

In this paper we propose a statistical modeling approach for phase detection of normal breathing sounds. Previous studies have been considering only the detection of inspiration mid-points and breathing onset. Here we focus on the detection of both inspiration and expiration phases. Based on an accurate statistical study of breathing signals, we suggest a nomenclature of respiratory cycle in a modeling perspective by adding a transitional phase between the inspiration and expiration phases. Thus, we put forward a new processing chain using improved Markov model in a bayesian framework in order to segment the signal and to detect the phases. We adapt the recent triplet Markov chain by exploiting priors on the respiratory cycle structure. Experiments on real respiratory signals show encouraging results.

Generalized higher-order nonlinear energy operators

We extend and generalize the Teager-Kaiser [in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (1993), Vol. 3, p. 149] and the higher-order differential energy operators [IEEE Signal Process. Lett.2, 152 (1995)] to a large class of operators called higher-order energy operators. We show that for AM-FM signal demodulation, the introduced partial derivative orders have to satisfy certain conditions. These operators are parameterized for local processing of AM-FM signals. The operators are illustrated using synthetic signals and a real signal from light scanning interferometry.

Two-dimensional continuous higher-order energy operators

An extension of the 2-D discrete Teager-Kaiser energy operator and the 1-D higher-order energy operators to the 2-D continuous case is proposed. These 2-D continuous operators are flexible enough to apply a large class of image gradient filters, and consequently different discrete energy operators are derived. Particularly, the proposed model takes into account the diagonal directions, through the partial derivatives. The obtained operators are computationally very simple, like the classical 2D Teager-Kaiser operator, and are well suited for image-processing applications such as image demodulation or image contrast enhancement. Results of demodulation of synthetic and real images, to estimate envelope information, are presented to show the feasibility of the proposed operators.

Local frequency and envelope estimation by Teager-Kaiser energy operators in white-light scanning interferometry.

In this work, a new method for surface extraction in white light scanning interferometry (WLSI) is introduced. The proposed extraction scheme is based on the Teager-Kaiser energy operator and its extended versions. This non-linear class of operators is helpful to extract the local instantaneous envelope and frequency of any narrow band AM-FM signal. Namely, the combination of the envelope and frequency information, allows effective surface extraction by an iterative re-estimation of the phase in association with a new correlation technique, based on a recent TK cross-energy operator. Through the experiments, it is shown that the proposed method produces substantially effective results in term of surface extraction compared to the peak fringe scanning technique, the five step phase shifting algorithm and the continuous wavelet transform based method. In addition, the results obtained show the robustness of the proposed method to noise and to the fluctuations of the carrier frequency.