Random field geometry has proven relevant results in the context of statistical hypothesis test for solving detection problems in signal and image processing. This paper emphasizes an unsupervised target detection problem in hyperspectral noisy images with very low signal-to-noise ratio (SNR) conditions. The targets have unknown spectral signatures located at unknown bandwidths and positions. To this aim, a spatio-spectral Gaussian random field (SS-GRF) model is proposed to provide a statistical inference about these targets in the full hyperspectral space by means of the geometric features of the noise, notably the expected Euler-characteristic (EC). The performance of the proposed method is demonstrated by the ROC curve analysis on synthetic examples, and confirms its efficiency and capacity to detect hyperspectral targets (astrophysical objects, remote sensing targets). At the end, we discuss the impact of the spectral dimensions on the method.

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.

We present in this paper a new narrowband image demodulation method. Our approach is based on the 2D higher order Teager-Kaiser operators (HOTKO). We show that the introduction of higher orders in the Teager-Kaiser operator, improves a lot the demodulation results, in comparison to the discrete energy separation algorithm (DESA) and the analytic image (AI) method. More precisely, for synthetic images, we show that the approximation errors on both the amplitude and the frequency components are much more lower with our proposed demodulation method than the DESA and the AI method. Moreover, it turns out that for the presented real images, the algorithm is so efficient, especially the amplitude counterpart, that it tracks the most important parts in images, and segments the regions of interest. We show how the algorithm could be used in sonar images for extracting mines'shadows, which is very important for both military and civil applications.

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.

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.

The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by hidden fuzzy Markov fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called iterative conditional estimation (ICE). This has been successfully applied in classical hidden Markov fields based segmentations. The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.