Algorithme de reconstruction itératif pour tomographie optique diffuse avec mesures dans le domaine temporel

L'imagerie par tomographie optique diffuse requiert de modéliser la propagation de la lumière dans un tissu biologique pour une configuration optique et géométrique donnée. On appelle cela le problème direct. Une nouvelle approche basée sur la méthode des différences finies pour modéliser numériquement via l'équation de la diffusion (ED) la propagation de la lumière dans le domaine temporel dans un milieu inhomogène 3D avec frontières irrégulières est développée pour le cas de l'imagerie intrinsèque, c'est-à-dire l'imagerie des paramètres optiques d'absorption et de diffusion d'un tissu. Les éléments finis, lourds en calculs, car utilisant des maillages non structurés, sont généralement préférés, car les différences finies ne permettent pas de prendre en compte simplement des frontières irrégulières. L'utilisation de la méthode de blocking-off ainsi que d'un filtre de Sobel en 3D peuvent en principe permettre de surmonter ces difficultés et d'obtenir des équations rapides à résoudre numériquement avec les différences finies. Un algorithme est développé dans le présent ouvrage pour implanter cette approche et l'appliquer dans divers cas puis de la valider en comparant les résultats obtenus à ceux de simulations Monte-Carlo qui servent de référence. L'objectif ultime du projet est de pouvoir imager en trois dimensions un petit animal, c'est pourquoi le modèle de propagation est au coeur de l'algorithme de reconstruction d'images. L'obtention d'images requière la résolution d'un problème inverse de grandes dimensions et l'algorithme est basé sur une fonction objective que l'on minimise de façon itérative à l'aide d'une méthode basée sur le gradient. La fonction objective mesure l'écart entre les mesures expérimentales faites sur le sujet et les prédictions de celles-ci obtenues du modèle de propagation. Une des difficultés dans ce type d'algorithme est l'obtention du gradient. Ceci est fait à l'aide de variables auxiliaire (ou adjointes). Le but est de développer et de combiner des méthodes qui permettent à l'algorithme de converger le plus rapidement possible pour obtenir les propriétés optiques les plus fidèles possible à la réalité capable d'exploiter la dépendance temporelle des mesures résolues en temps, qui fournissent plus d'informations tout autre type de mesure en TOD. Des résultats illustrant la reconstruction d'un milieu complexe comme une souris sont présentés pour démontrer le potentiel de notre approche.

On precise three-dimensional environment modeling via UAV-based photogrammetric systems

Abstract : Images acquired from unmanned aerial vehicles (UAVs) can provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modeling. Solutions developed for this purpose are mainly operating based on photogrammetry concepts, namely UAV-Photogrammetry Systems (UAV-PS). Such systems are used in applications where both geospatial and visual information of the environment is required. These applications include, but are not limited to, natural resource management such as precision agriculture, military and police-related services such as traffic-law enforcement, precision engineering such as infrastructure inspection, and health services such as epidemic emergency management. UAV-photogrammetry systems can be differentiated based on their spatial characteristics in terms of accuracy and resolution. That is some applications, such as precision engineering, require high-resolution and high-accuracy information of the environment (e.g. 3D modeling with less than one centimeter accuracy and resolution). In other applications, lower levels of accuracy might be sufficient, (e.g. wildlife management needing few decimeters of resolution). However, even in those applications, the specific characteristics of UAV-PSs should be well considered in the steps of both system development and application in order to yield satisfying results. In this regard, this thesis presents a comprehensive review of the applications of unmanned aerial imagery, where the objective was to determine the challenges that remote-sensing applications of UAV systems currently face. This review also allowed recognizing the specific characteristics and requirements of UAV-PSs, which are mostly ignored or not thoroughly assessed in recent studies. Accordingly, the focus of the first part of this thesis is on exploring the methodological and experimental aspects of implementing a UAV-PS. The developed system was extensively evaluated for precise modeling of an open-pit gravel mine and performing volumetric-change measurements. This application was selected for two main reasons. Firstly, this case study provided a challenging environment for 3D modeling, in terms of scale changes, terrain relief variations as well as structure and texture diversities. Secondly, open-pit-mine monitoring demands high levels of accuracy, which justifies our efforts to improve the developed UAV-PS to its maximum capacities. The hardware of the system consisted of an electric-powered helicopter, a high-resolution digital camera, and an inertial navigation system. The software of the system included the in-house programs specifically designed for camera calibration, platform calibration, system integration, onboard data acquisition, flight planning and ground control point (GCP) detection. The detailed features of the system are discussed in the thesis, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The accuracy of the results was evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy were assessed. The second part of this thesis concentrates on improving the techniques of sparse and dense reconstruction. The proposed solutions are alternatives to traditional aerial photogrammetry techniques, properly adapted to specific characteristics of unmanned, low-altitude imagery. Firstly, a method was developed for robust sparse matching and epipolar-geometry estimation. The main achievement of this method was its capacity to handle a very high percentage of outliers (errors among corresponding points) with remarkable computational efficiency (compared to the state-of-the-art techniques). Secondly, a block bundle adjustment (BBA) strategy was proposed based on the integration of intrinsic camera calibration parameters as pseudo-observations to Gauss-Helmert model. The principal advantage of this strategy was controlling the adverse effect of unstable imaging networks and noisy image observations on the accuracy of self-calibration. The sparse implementation of this strategy was also performed, which allowed its application to data sets containing a lot of tie points. Finally, the concepts of intrinsic curves were revisited for dense stereo matching. The proposed technique could achieve a high level of accuracy and efficiency by searching only through a small fraction of the whole disparity search space as well as internally handling occlusions and matching ambiguities. These photogrammetric solutions were extensively tested using synthetic data, close-range images and the images acquired from the gravel-pit mine. Achieving absolute 3D mapping accuracy of 11±7 mm illustrated the success of this system for high-precision modeling of the environment.