Deconvolution of Ultrasonic Signals in Porous Materials: Estimation of Acoustic Propagation Parameters andWave Separation.

Our study focuses on the development of assessment tools and nondestructive evaluation of porous materials from ultrasonic measurements. These materials are encountered in many industrial applications such as polyurethane foam used for insulation, aluminum foams used in aerospace or cancellous bone for biological applications. Acoustic propagation in these complex heterogeneous materials is governed by the Biot theory [1], involving the propagation of two types of waves: slow and fast wave, whose properties are respectively related to the fluid and solid phases constituting the material. During the propagation, these waves undergo deformations that can be characterized by related propagation models [2], defined by specific frequency-dependent attenuation and dispersion laws. Identification of these waves and of their related propagation parameters then provides a characterization of the material health. This may be a difficult problem in the case of porous materials of low thickness and/or with defects, since the different waves and their echoes may overlap, as shown in the example in Figure 1. Separation of these waveforms should however be possible, by taking into account reliable models describing the propagation of each wave. This paper presents a method for identifying such waves (arrival times and propagation parameters) from signals acquired in transmission or reflection, based on an optimization procedure that minimizes a nonlinear least-squares criterion, which is sufficiently constrained and properly initialized in order to produce robust results. The method is validated with numerical simulations and applied to a laboratory experiment with a porous ceramic plate. This work is partially supported by the French region “Pays de la Loire”, through the DECIMAP project

Automatic image annotation system using deep learning method to analyse ambiguous images

Image annotation has gotten a lot of attention recently because of how quickly picture data has expanded. Together with image analysis and interpretation, image annotation, which may semantically describe images, has a variety of uses in allied industries including urban planning engineering. Even without big data and image identification technologies, it is challenging to manually analyze a diverse variety of photos. The improvements to the Automated Image Annotation (AIA) label system have been the subject of several scholarly research. The authors will discuss how to use image databases and the AIA system in this essay. The proposed method extracts image features from photos using an improved VGG-19, and then uses nearby features to automatically forecast picture labels. The proposed study accounts for both correlations between labels and images as well as correlations within images. The number of labels is also estimated using a label quantity prediction (LQP) model, which improves label prediction precision. The suggested method addresses automatic annotation methodologies for pixel-level images of unusual things while incorporating supervisory information via interactive spherical skins. The genuine things that were converted into metadata and identified as being connected to pre-existing categories were categorized by the authors using a deep learning approach called a conventional neural network (CNN) - supervised. Certain object monitoring systems strive for a high item detection rate (true-positive), followed by a low availability rate (false-positive). The authors created a KD-tree based on k-nearest neighbors (KNN) to speed up annotating. In order to take into account for the collected image backdrop. The proposed method transforms the conventional two-class object detection problem into a multi-class classification problem, breaking the separated and identical distribution estimations on machine learning methodologies. It is also simple to use because it only requires pixel information and ignores any other supporting elements from various color schemes. The following factors are taken into consideration while comparing the five different AIA approaches: main idea, significant contribution, computational framework, computing speed, and annotation accuracy. A set of publicly accessible photos that serve as standards for assessing AIA methods is also provided, along with a brief description of the four common assessment signs

Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic

In this article, we propose a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, we provide the distribution of the DRT statistic that is exactly Wilks's lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric SAR (PolSAR) data are employed to assess the performance of the method and is compared with the Hotelling-Lawley trace (HLT) statistic and the likelihood ratio test (LRT) statistic

Nouveau modèle de texture markovien basé sur la loi K : Application à l'echographie

- On propose dans cet article une nouvelle modélisation de la texture issue de l'imagerie cohérente basée sur les champs de Markov. Ce modèle permet de prendre en compte le comportement spatial de la texture tout en conservant une distribution locale de type loi K. Afin de ce respecter la nature des intensités lumineuses, on choisit de se placer localement dans le cadre du modèle produit. Ainsi, la construction de ce modèle passe par celle d'un champ markovien de distribution locale gamma. Ce dernier modèle est volentairement choisi en dehors de la classe des auto-modèles de Besag afin de pouvoir contrôler façilement sa moyenne locale. Afin de comprendre le rôle des paramètres du champ markovien K, un ensemble de textures synthétiques sont ici présentées. On procède ensuite à une première validation du modèle par la synthèse de textures échographiques. Ce modèle est ensuite testé sur des textures extraites d'images échographiques

Caractérisation de texture d'échographie RF par champ markovien.

The ultrasound is a medical imaging tool that imposes itself for the diagnosis of numerous pathologies. As a consequence, many image filed studies are concerned with these images to provide tools of analysis and tissue characterization. The objective of this thesis is to exploit spatial markovien models representing the ultrasound texture that exist in the image to extract susceptible interactions that would describe the organization of these textures. In the first part, we evaluate the efficiency of the distributions proposed in the previous study modeling the RF envelope amplitude.We illustrate, through texture simulations, the links between the parameters of these distributions and the parameters of the scatterers, namely the density, the amplitude and the spacing. In the second part, we elaborate our texture spatial models inspired by the probability modeling RF envelope amplitude. So we obtain in every pixel of the image a local distribution of type K or Nakagami. Simulation and parameter estimation were developed. The third part is dedicated to the application of the spatial models on RF simulated images. We show here the adequate models for the description of the spatial arrangement of the scatterers constituting the tissue, and the connection of the model parameters with the intrinsic properties of the scatterers. Finally, the future development of this approach are to be discussed.L'échographie est un outil d'imagerie médicale qui s'impose pour le diagnostic de nombreuses pathologies. En conséquence, une large littérature du domaine de l'image s'intéresse à ces images pour fournir des outils d'analyse et de caractérisation tissulaire. L'objectif de cette thèse est de modéliser la texture échographique par des champs markoviens pour en extraire des paramètres susceptibles de caractériser l'organisation des tissus. Dans une première partie, nous évaluons l'habilité des lois de distribution proposées dans la littérature (Gamma, K et Nakagami) à modéliser les niveaux de l'enveloppe du signal RF. Nous illustrons par des simulations de texture échographique les liens entre les paramètres de ces lois et les paramètres intrinsèques des diffuseurs, à savoir la densité, l'amplitude et l'espacement entre diffuseurs. Dans une deuxième partie, nous élaborons des modèles spatiaux par l'approche markovienne de façon à obtenir en chaque pixel de l'image une distribution de type K ou Nakagami dont les paramètres dépendent de la configuration du voisinage. A l'aide de simulation de ces champs de micro-texture, on illustre le comportement de leurs paramètres. La troisième partie est dédiée à l'application de ces modèles spatiaux sur des images enveloppes simulées. On montre ici l'habilité de ces modèles à décrire la disposition spatiale des diffuseurs qui constituent le tissu, et le lien entre les paramètres du modèle et les propriétés intrinsèques de diffuseurs. Finalement, les développements futurs de cette approche sont discutés

Maximum-Likelihood Parameter Estimation of the Product Model for Multilook Polarimetric SAR Data

International audienceThe product model is assumed to be an appropriate statistical model for multilook polarimetric synthetic radar data (PolSAR). According to this model, the observed signal is considered as the product of independent random variates of a complex Gaussian speckle and a non-Gaussian texture. With different texture distributions, the product model leads to different expressions for the compound distribution considered as an infinite mixture model. In this paper, the maximum-likelihood (ML) estimator is derived to jointly estimate the speckle and texture parameters in the compound distribution model using the multilook polarimetric radar data. In particular, we estimate: 1) the equivalent number of looks; 2) the covariance matrix of the speckle component; and 3) the texture distribution parameters. The expectation-maximization algorithm is developed to compute the ML estimates of the unknown parameters. The hybrid Cramer-Rao bounds (HCRBs) are also derived for these parameters. First, a general HCRB expression is derived under an arbitrary texture distribution. Then, this expression is simplified for a specific texture distribution. The performance of the ML is compared with the performance of other known estimators using the simulated and real multilook PolSAR data. For real data, a goodness of fit of multilook PolSAR data histograms is used to assess the fitting accuracy of the compound distributions using different estimators

Multilook Polarimetric SAR Change Detection Using Stochastic Distances Between Matrix-Variate Gd⁰ Distributions

International audienceIn this article, we propose an efficient heterogeneous change detection algorithm based on stochastic distance measure between two G 0 d distributions. Due to its flexibility and simplicity, the matrix-variate G 0 d distribution has been successfully used to model the multilook polarimetric synthetic aperture radar (PolSAR) data and has been tested for classification, segmen-tation, and image analysis. Concretely, closed-form expressions for the Kullback-Leibler, Rényi of order β, Bhattacharyya, and Hellinger distances are provided to compute the stochastic distance between G 0 d distributions. In this context, we resort to the expectation-maximization (EM) to estimate accurately with low complexity the parameters of the probability distribution of the two multilook polarimetric covariance matrices to be compared. Finally, the performance of the method is compared firstly to the performance of other known distributions, such as the scaled complex Wishart distribution, and secondly to other known statistical tests using simulated and real multilook PolSAR data. Index Terms-Bhattacharyya and Hellinger distances, change detection, expectation-maximization (EM) algorithm, multilook polarimetric synthetic aperture radar (PolSAR) data, Rényi of order β, stochastic distances: Kullback-Leibler

Kullback–Leibler Divergence Between Multivariate Generalized Gaussian Distributions

International audienceThe Kullback-Leibler divergence (KLD) between two multivariate generalized Gaussian distributions (MGGDs) is a fundamental tool in many signal and image processing applications. Until now, the KLD of MGGDs has no known explicit form, and it is in practice either estimated using expensive Monte-Carlo stochastic integration or approximated. The main contribution of this letter is to present a closed-form expression of the KLD between two zero-mean MGGDs. Depending on the Lauricella series, a simple way of calculating numerically the KLD is exposed. Finally, we show that the approximation of the KLD by Monte-Carlo sampling converges to its theoretical value when the number of samples goes to the infinity

A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions

This paper introduces a closed-form expression for the Kullback–Leibler divergence (KLD) between two central multivariate Cauchy distributions (MCDs) which have been recently used in different signal and image processing applications where non-Gaussian models are needed. In this overview, the MCDs are surveyed and some new results and properties are derived and discussed for the KLD. In addition, the KLD for MCDs is showed to be written as a function of Lauricella D-hypergeometric series FD(p). Finally, a comparison is made between the Monte Carlo sampling method to approximate the KLD and the numerical value of the closed-form expression of the latter. The approximation of the KLD by Monte Carlo sampling method are shown to converge to its theoretical value when the number of samples goes to the infinity