DETECTION ET REHAUSSEMENT DES FAILLES DANS LES BLOCS SISMIQUES PAR PROCESSUS OBJET

Nous proposons une nouvelle approche pour extraire le réseau de faille dans un bloc sismique 3D. Notre approche est fondée sur un processus ponctuel marqué en considérant un modèle a priori dans lequel les failles sont des segments fins connectés entre eux. L'attache aux données utilisée peut être inspirée d'attributs de détection de faille classiques leur conférant ainsi un caractère multi-échelle. De plus, l'utilisation conjointe de plusieurs attributs peut nous permettre de définir notre processus comme un processus de fusion de décision. Enfin, en considérant le réseau obtenu comme le résultat d'un processus de segmentation, nous proposons d'itérer la réalisation de l'ensemble du processus de points dans le but d'en faire une méthode de rehaussement de la détection par un attribut faille

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features

A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes

High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable forestry managers to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the resources. Automatic algorithms are needed in that prospect to assist human operators in the exploitation of these data. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the locations of the trees and marks their geometric features. As a result we obtain the number of stems, their position, and their size. This approach yields an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted, in 2D and 3D. Results are shown on Colour Infrared aerial images provided by the French National Forest Inventory (IFN

Flamingo detection using Marked Point Processes for estimating the size of populations

In this paper, we present a new technique to automatically detect and count breeding Greater flamingos (Phoenicopterus roseus) on aerial photographs of their colonies. We consider a stochastic approach based on marked point processes also called object processes. Here, the objects represent flamingos which are defined as ellipses. The Gibbs density associated with the marked point process of ellipses is defined w.r.t the Poisson measure. Thus, the issue is reduced to an energy minimization, where the energy is composed of a regularization term (prior density), which introduces some constraints on the objects and their interactions, and a data term, which links the objects to the features to be extracted in the image. The prior energy is defined as a sum of local energies for each object. For a given object o, we consider the set S(o) of objects in the current configuration which overlap o. An overlapping coefficient between two objects is defined by the intersection area normalised by the minimum size between the two objects. The local energy, associated to o, is then proportional to the maximum overlapping coefficient between o and any element of S(o). The data term is also defined by a sum local energies over each object in the configuration. The local energy is obtained from the computation of a radiometric distance between pixels in the ellipse, modeling the flamingo, and pixels in the neighborhood of this ellipse....Nous présentons dans cet article une nouvelle technique de détection de flamants roses sur des images aériennes. Nous considérons une approche stochastique fondée sur les processus ponctuels marqués, aussi appelés processus objets. Ici, les objets représentent les flamants, qui sont modélisés par des ellipses. La densité associée au processus ponctuel marqué d'ellipses est définie par rapport à une mesure de Poisson. Dans un cadre gibbsien, le problème se réduit à la minimisation d'une énergie, qui est constituée d'un terme de régularisation (densité a priori), qui introduit des contraintes sur les objets et leurs interactions; et un terme d'attache aux données, qui permet de localiser sur l'image les flamants à extraire. Nous échantillonnons le processus pour extraire la configuration d'objets minimisant l'énergie grâce à une nouvelle dynamique de Naissances et Morts multiples, amenant finalement à une estimation du nombre total de flamants présents sur l'image. Cette approche donne des comptes avec une bonne précision comparée aux comptes manuels. De plus, elle ne nécessite aucun traitement préalable ou intervention manuelle, ce qui réduit considérablement le temps d'obtention des comptes

Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires

National audiencePreviously, marked point process models have been successfully applied to solve problems of feature network extraction from aerial and satellite high resolution photographs. The main advantage of these models is that they take into account object geometry. In particular, objects in the image are represented by a set of geometric shapes which are governed by two types of energy: a data energy term that links objects to the processed image, and a regularizing energy term which introduces some prior knowledge about the observed scene. Furthermore, some parameters reflecting the importance of these energies are involved in the definition of this model. Thus, to ensure automatic object extraction, an estimation method of these parameters, based on the Stochastic Expectation-Maximization algorithm was studied and showed promising results on a simple example of a marked point process where the considered objects are disks. We propose to extend this study to extract more general geometric shapes, such as ellipses and rectangles. Hence, we deal with several applications, namely flamingo extraction, tree crown extraction, detection of boats in a seaport, building footprint detection and refugee tent detection. The main originality of this work consists in developing new energy components that allow to model boat alignment and building interactions within an unsupervised framework.Les modèles de processus ponctuels marqués ont été précédemment appliqués avec succès pour résoudre des problèmes d'extraction de réseaux de formes à partir d'images aériennes et satellitaires haute résolution. L'avantage de ces modèles est qu'ils prennent en compte la géométrie des objets à extraire. En particulier, les objets de l'image sont représentés par un ensemble de formes géométriques dont la disposition est gouvernée par deux types d'énergies : une énergie d'attache aux données qui lie les objets à l'image traitée, et une énergie de régularisation qui permet d'introduire des connaissances a priori sur le réseau d'objets. Par ailleurs, des paramètres qui traduisent l'influence de ces énergies sont introduits dans la définition de ce modèle. Afin d'assurer une extraction automatique des objets, une méthode d'estimation des paramètres en question, fondée sur une version stochastique de l'algorithme Espérance-Maximisation, a été étudiée et a conduit à des résultats prometteurs sur un exemple simple de processus ponctuels d'objets circulaires. Nous proposons de prolonger cette étude afin d'extraire des formes géométriques plus générales, i.e. des formes elliptiques et rectangulaires. Nous abordons ainsi plusieurs applications ; à savoir l'extraction de flamants roses, de houppiers d'arbres, de bateaux dans un port marîtime, de la trace au sol du bâti ainsi que la détection de tentes de réfugiés. L'originalité majeure de ce travail réside dans la définition des composantes de l'énergie qui permettent de modéliser l'alignement des navires à quai, ainsi que les interactions qui existent entre les bâtis tout en restant dans un cadre automatique

On-the-fly olive tree counting using a UAS and cloud services

Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F1 score of 99.09% for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flightPeer ReviewedPostprint (published version

Data Science Methods for Analyzing Nanomaterial Images and Videos

A large amount of nanomaterial characterization data has been routinely collected by using electron microscopes and stored in image or video formats. A bottleneck in making effective use of the image/video data is the lack of the development of sophisticated data science methods capable of unlocking valuable material pertinent information buried in the raw data. To address this problem, the research of this dissertation begins with understanding the physical mechanisms behind the concerned process to determine why the generic methods fall short. Afterwards, it designs and improves image processing and statistical modeling tools to address the practical challenges. Specifically, this dissertation consists of two main tasks: extracting useful information from images or videos of nanomaterials captured by electron microscopes, and designing analytical methods for modeling/monitoring the dynamic growth of nanoparticles. In the first task, a two-pipeline framework is proposed to fuse two kinds of image information for nanoscale object detection that can accurately identify and measure nanoparticles in transmission electron microscope (TEM) images of high noise and low contrast. To handle the second task of analyzing nanoparticle growth, this dissertation develops dynamic nonparametric models for time-varying probability density functions (PDFs) estimation. Unlike simple statistics, a PDF contains fuller information about the nanoscale objects of interests. Characterizing the dynamic changes of the PDF as the nanoparticles grow into different sizes and morph into different shapes, the proposed nonparametric methods are capable of analyzing an in situ TEM video to delineate growth stages in a retrospective analysis, or tracking the nanoparticle growth process in a prospective analysis. The resulting analytic methods have applications in areas beyond the nanoparticle growth process such as the image-based process control tasks in additive manufacturing

Parallel Markov Chain Monte Carlo

The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very highdimensional integrals. As such MCMC has found a wide variety of applications in fields including computational biology and physics,financial econometrics, machine learning and image processing. This thesis presents a number of new method for reducing the runtime of Markov Chain Monte Carlo simulations by using SMP machines and/or clusters. Two of the methods speculatively perform iterations in parallel, reducing the runtime of MCMC programs whilst producing statistically identical results to conventional sequential implementations. The other methods apply only to problem domains that can be presented as an image, and involve using various means of dividing the image into subimages that can be proceed with some degree of independence. Where possible the thesis includes a theoretical analysis of the reduction in runtime that may be achieved using our technique under perfect conditions, and in all cases the methods are tested and compared on selection of multi-core and multi-processor architectures. A framework is provided to allow easy construction of MCMC application that implement these parallelisation methods

Parallel Markov Chain Monte Carlo

The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very highdimensional integrals. As such MCMC has found a wide variety of applications in fields including computational biology and physics,financial econometrics, machine learning and image processing. This thesis presents a number of new method for reducing the runtime of Markov Chain Monte Carlo simulations by using SMP machines and/or clusters. Two of the methods speculatively perform iterations in parallel, reducing the runtime of MCMC programs whilst producing statistically identical results to conventional sequential implementations. The other methods apply only to problem domains that can be presented as an image, and involve using various means of dividing the image into subimages that can be proceed with some degree of independence. Where possible the thesis includes a theoretical analysis of the reduction in runtime that may be achieved using our technique under perfect conditions, and in all cases the methods are tested and compared on selection of multi-core and multi-processor architectures. A framework is provided to allow easy construction of MCMC application that implement these parallelisation methods.EThOS - Electronic Theses Online ServiceUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo