La présente dissertation est principalement consacrée à la conception et à la caractérisation d’une sonde universelle pour l’imagerie volumétrique ultrasons-optoacoustique et le développement d’un algorithme de reconstruction adapté aux exigences physiques pour la conception du système. Les traits distinctifs de cette dissertation sont l’introduction d’une nouvelle géométrie pour les sondes manuelles ultrasons-optoacoustique et des évaluations systématiques basées sur des méthodes de pré-reconstruction et post-reconstruction. Pour éviter l’interprétation biaisée, une évaluation capable d’évaluer le potentiel de la sonde doit être faite. Les caractéristiques mentionnées établissent un cadre pour l’évaluation des performances du système d’imagerie d’une manière précise. En outre, elle permet d’optimiser les performances suivant l’objectif fixé. Ainsi, deux algorithmes de reconstruction anticipée ont été élaborés pour la conception du système OPUS (optoacoustique ultrasons) capables de produire des images avec un contraste et une résolution homogènes sur tout le volume d’intérêt. L’intérêt d’avoir de tels algorithmes est principalement dû au fait que l’analyse des données médicales est souvent faite dans des conditions difficiles, car on est face au bruit, au faible contraste, aux projections limités et à des transformations indésirables opérées par les systèmes d’acquisition. Cette thèse montre, aussi, comment les artefacts de reconstruction peuvent être réduits en compensant les propriétés d’ouverture et en atténuant les artefacts dus à l’échantillonnage angulaire parcimonieux. Afin de transférer cette méthodologie à la clinique et de valider les résultats théoriques, une plate-forme d’imagerie expérimentale a été développée. En utilisant le système de mesure développé, l’évolution d’une nouvelle géométrie annulaire parcimonieuse et sa dynamique ont été étudiées et une preuve de concept a été démontrée à travers des mesures expérimentales dans le but d’évaluer les progrès réalisés. / When the interest is in multiscale and multipurpose imaging, there exists such a will in integrating multi-modalilties into a synergistic paradigm in order to leverage the diagnostic values of the interrogating agents. Employing multiple wavelengths radiation, optoacoustic imaging benefits from the optical contrast to specifically resolve molecular structure of tissue in a non-invasive manner. Hybridizing optoacoustic and ultrasound imaging comes with the promises of delivering the complementary morphological, functional and metabolic information of the tissue. This dissertation is mainly devoted to the design and characterization of a hybridized universal handheld probe for optoacoustic ultrasound volumetric imaging and developing adaptive reconstruction algorithms toward the physical requirements of the designed system. The distinguishing features of this dissertation are the introduction of a new geometry for optoacoustic ultrasonic handheld probe and systematic assessments based on pre and post reconstruction methods. To avoid the biased interpretation, a de facto performance assessment being capable of evaluating the potentials of the designed probe in an unbiased manner must be practiced. The aforementioned features establish a framework for characterization of the imaging system performance in an accurate manner. Moreover, it allows further task performance optimization as well. Correspondingly, two advanced reconstruction algorithms have been elaborated towards the requirement of the designed optoacoustic-ultrasound (OPUS) imaging system in order to maximize its ability to produce images with homogeneous contrast and resolution over the entire volume of interest. This interest is mainly due to the fact that the medical data analysis pipeline is often carried out in challenging conditions, since one has to deal with noise, low contrast, limited projections and undesirable transformations operated by the acquisition system. The presented thesis shows how reconstruction artifacts can be reduced by compensating for the detecting aperture properties and alleviate artifacts due to sparse angular sampling. In pursuit of transferring this methodology to clinic and validating the theoretical results, a synthetic imaging platform was developed. Using the measurement system, the evolution of a novel sparse annular geometry and its dynamics has been investigated and a proof of concept was demonstrated via experimental measurement with the intention of benchmarking progress.

Cette thèse a été réalisée dans le secteur de l'industrie automobile, en collaboration avec le Groupe Renault et concerne en particulier le développement de systèmes d'aide à la conduite avancés et de véhicules autonomes. Les progrès réalisés par la communauté scientifique durant les dernières décennies, dans les domaines de l'informatique et de la robotique notamment, ont été si importants qu'ils permettent aujourd'hui la mise en application de systèmes complexes au sein des véhicules. Ces systèmes visent dans un premier temps à réduire les risques inhérents à la conduite en assistant les conducteurs, puis dans un second temps à offrir des moyens de transport entièrement autonomes. Les méthodes de SLAM multi-objets actuellement intégrées au sein de ces véhicules reposent pour majeure partie sur l'utilisation de capteurs embarqués très performants tels que des télémètres laser, au coût relativement élevé. Les caméras numériques en revanche, de par leur coût largement inférieur, commencent à se démocratiser sur certains véhicules de grande série et assurent généralement des fonctions d'assistance à la conduite, pour l'aide au parking ou le freinage d'urgence, par exemple. En outre, cette implantation plus courante permet également d'envisager leur utilisation afin de reconstruire l'environnement dynamique proche des véhicules en trois dimensions. D'un point de vue scientifique, les techniques de SLAM visuel multi-objets existantes peuvent être regroupées en deux catégories de méthodes. La première catégorie et plus ancienne historiquement concerne les méthodes stéréo, faisant usage de plusieurs caméras à champs recouvrants afin de reconstruire la scène dynamique observée. La plupart reposent en général sur l'utilisation de paires stéréo identiques et placées à faible distance l'une de l'autre, ce qui permet un appariement dense des points d'intérêt dans les images et l'estimation de cartes de disparités utilisées lors de la segmentation du mouvement des points reconstruits. L'autre catégorie de méthodes, dites monoculaires, ne font usage que d'une unique caméra lors du processus de reconstruction. Cela implique la compensation du mouvement propre du système d'acquisition lors de l'estimation du mouvement des autres objets mobiles de la scène de manière indépendante. Plus difficiles, ces méthodes posent plusieurs problèmes, notamment le partitionnement de l'espace de départ en plusieurs sous-espaces représentant les mouvements individuels de chaque objet mobile, mais aussi le problème d'estimation de l'échelle relative de reconstruction de ces objets lors de leur agrégation au sein de la scène statique. La problématique industrielle de cette thèse, consistant en la réutilisation des systèmes multi-caméras déjà implantés au sein des véhicules, majoritairement composés d'un caméra frontale et de caméras surround équipées d'objectifs très grand angle, a donné lieu au développement d'une méthode de reconstruction multi-objets adaptée aux systèmes multi-caméras hétérogènes en stéréo wide-baseline. Cette méthode est incrémentale et permet la reconstruction de points mobiles éparses, grâce notamment à plusieurs contraintes géométriques de segmentation des points reconstruits ainsi que de leur trajectoire. Enfin, une évaluation quantitative et qualitative des performances de la méthode a été menée sur deux jeux de données distincts, dont un a été développé durant ces travaux afin de présenter des caractéristiques similaires aux systèmes hétérogènes existants. / This Ph.D. thesis, which has been carried out in the automotive industry in association with Renault Group, mainly focuses on the development of advanced driver-assistance systems and autonomous vehicles. The progress made by the scientific community during the last decades in the fields of computer science and robotics has been so important that it now enables the implementation of complex embedded systems in vehicles. These systems, primarily designed to provide assistance in simple driving scenarios and emergencies, now aim to offer fully autonomous transport. Multibody SLAM methods currently used in autonomous vehicles often rely on high-performance and expensive onboard sensors such as LIDAR systems. On the other hand, digital video cameras are much cheaper, which has led to their increased use in newer vehicles to provide driving assistance functions, such as parking assistance or emergency braking. Furthermore, this relatively common implementation now allows to consider their use in order to reconstruct the dynamic environment surrounding a vehicle in three dimensions. From a scientific point of view, existing multibody visual SLAM techniques can be divided into two categories of methods. The first and oldest category concerns stereo methods, which use several cameras with overlapping fields of view in order to reconstruct the observed dynamic scene. Most of these methods use identical stereo pairs in short baseline, which allows for the dense matching of feature points to estimate disparity maps that are then used to compute the motions of the scene. The other category concerns monocular methods, which only use one camera during the reconstruction process, meaning that they have to compensate for the ego-motion of the acquisition system in order to estimate the motion of other objects. These methods are more difficult in that they have to address several additional problems, such as motion segmentation, which consists in clustering the initial data into separate subspaces representing the individual movement of each object, but also the problem of the relative scale estimation of these objects before their aggregation within the static scene. The industrial motive for this work lies in the use of existing multi-camera systems already present in actual vehicles to perform dynamic scene reconstruction. These systems, being mostly composed of a front camera accompanied by several surround fisheye cameras in wide-baseline stereo, has led to the development of a multibody reconstruction method dedicated to such heterogeneous systems. The proposed method is incremental and allows for the reconstruction of sparse mobile points as well as their trajectory using several geometric constraints. Finally, a quantitative and qualitative evaluation conducted on two separate datasets, one of which was developed during this thesis in order to present characteristics similar to existing multi-camera systems, is provided.

[pt] O presente trabalho apresenta um estudo sobre técnicas de construção de um modelo tridimensional de objetos a partir unicamente da informação de textura. Estas técnicas são baseadas na medida da deformação da textura ao longo de uma superfície, obtendo assim a orientação do vetor normal à superfície em cada ponto. De posse da orientação é possível construir um modelo tridimensional do objeto. São avaliados três métodos. O primeiro emprega Filtros de Gabor e momentos de segunda ordem como medida de textura e os outros dois estimam a transformação afim entre recortes de igual tamanho na imagem. A estimativa da transformação afim tem ênfase especial neste trabalho por ser um passo fundamental no algoritmo que mede a deformação da textura. Os métodos foram validados em diferentes etapas, de forma a avaliar: estimativa da transformação afim; decomposição em ângulos; e reconstrução do modelo 3D a partir do mapa de orientação, também conhecido como mapa de agulhas. A avaliação experimental foi realizada com imagens sintéticas e fotos de objetos reais. Os resultados mostram a aplicabilidade, dificuldades e restrições dos métodos analisados. / [en] The current work presents a study about methods for 3D object shape reconstruction based on their texture information. These methods, called Shape from Texture, measure texture deformation along object surface, obtaining the orientation in each point of the image. Having the orientation in each point (a needle map) it is possible to construct the object 3D model. Three methods are studied in this dissertation. One of these methods uses Gabor Filters and second order moments, and other two that estimate the affine transform between images patches. The affine estimation problem gets emphasis in the present work since it is an essential step in most Shape from Texture algorithms. The methods were tested in separate steps: evaluate the affine transform estimation; the decomposition of the affine matrix in slant and tilt angles; and the 3D model reconstruction using the needle map. Both synthetic and real images were used on the experiments. The results clearly show the applicability, difficulties and restrictions of the investigated methods.

While modern digital cameras incorporate sophisticated engineering, in terms of their core functionality, cameras have changed remarkably little in more than a hundred years. In particular, from a given viewpoint, conventional photography essentially remains limited to manipulating a basic set of controls: exposure time, focus setting, and aperture setting. In this dissertation we present three new methods in this domain, each based on capturing multiple photos with different camera settings. In each case, we show how defocus can be exploited to achieve different goals, extending what is possible with conventional photography. These methods are closely connected, in that all rely on analyzing changes in aperture. First, we present a 3D reconstruction method especially suited for scenes with high geometric complexity, for which obtaining a detailed model is difficult using previous approaches. We show that by controlling both the focus and aperture setting, it is possible compute depth for each pixel independently. To achieve this, we introduce the "confocal constancy" property, which states that as aperture setting varies, the pixel intensity of an in-focus scene point will vary in a scene-independent way that can be predicted by prior calibration. Second, we describe a method for synthesizing photos with adjusted camera settings in post-capture, to achieve changes in exposure, focus setting, etc. from very few input photos. To do this, we capture photos with varying aperture and other settings fixed, then recover the underlying scene representation best reproducing the input. The key to the approach is our layered formulation, which handles occlusion effects but is tractable to invert. This method works with the built-in "aperture bracketing" mode found on most digital cameras. Finally, we develop a "light-efficient" method for capturing an in-focus photograph in the shortest time, or with the highest quality for a given time budget. While the standard approach involves reducing the aperture until the desired region is in-focus, we show that by "spanning" the region with multiple large-aperture photos,we can reduce the total capture time and generate the in-focus photo synthetically. Beyond more efficient capture, our method provides 3D shape at no additional cost.

While modern digital cameras incorporate sophisticated engineering, in terms of their core functionality, cameras have changed remarkably little in more than a hundred years. In particular, from a given viewpoint, conventional photography essentially remains limited to manipulating a basic set of controls: exposure time, focus setting, and aperture setting. In this dissertation we present three new methods in this domain, each based on capturing multiple photos with different camera settings. In each case, we show how defocus can be exploited to achieve different goals, extending what is possible with conventional photography. These methods are closely connected, in that all rely on analyzing changes in aperture. First, we present a 3D reconstruction method especially suited for scenes with high geometric complexity, for which obtaining a detailed model is difficult using previous approaches. We show that by controlling both the focus and aperture setting, it is possible compute depth for each pixel independently. To achieve this, we introduce the "confocal constancy" property, which states that as aperture setting varies, the pixel intensity of an in-focus scene point will vary in a scene-independent way that can be predicted by prior calibration. Second, we describe a method for synthesizing photos with adjusted camera settings in post-capture, to achieve changes in exposure, focus setting, etc. from very few input photos. To do this, we capture photos with varying aperture and other settings fixed, then recover the underlying scene representation best reproducing the input. The key to the approach is our layered formulation, which handles occlusion effects but is tractable to invert. This method works with the built-in "aperture bracketing" mode found on most digital cameras. Finally, we develop a "light-efficient" method for capturing an in-focus photograph in the shortest time, or with the highest quality for a given time budget. While the standard approach involves reducing the aperture until the desired region is in-focus, we show that by "spanning" the region with multiple large-aperture photos,we can reduce the total capture time and generate the in-focus photo synthetically. Beyond more efficient capture, our method provides 3D shape at no additional cost.

The complicated underwater environment and the poor underwater vision make super-mini underwater cable robot hardly to be controlled. Traditionally, the manual control method by operators is adopted by this kind of robots. Unfortunately, the robots can hardly work normally in these practical circumstances. Therefore, to overcome these shortcomings and improve the abilities of these underwater cable robots, this paper proposes several improvements, including the system design, the motion controller design, three dimensional obstacle recognition and three dimensional path reconstruction technologies etc. The details are displayed as follow: (1) Super-mini underwater robot system design: several improvement schemes and important design ideas are investigated for the super-mini underwater robot.(2) Super-mini robot motion controller design: The motion controller design of underwater robot in complicated circumstance is investigated. A new adaptive neural network sliding mode controller with balanced parameter controller (ANNSMB) is proposed. Based on the theory of adaptive fuzzy sliding mode controller (AFSMC), an improved algorithm is also proposed and applied to the underwater robot. (3)Research of three dimensional underwater environment reconstructions: The algorithms and the experiments of underwater environment reconstructions are investigated. DT-MRI image processing algorithm and the theory of three dimensional obstacle reconstructions are adopted and improved for the application of the underwater robot. (4) The super-mini underwater robot path planning algorithms are investigated.

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Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.

The design techniques of the components on the macro level are established in the scientific community, however are far behind from the real material performance limits. To obtain those limits, the deeper understanding of the material structure is required. The methods of a new comonents production through standard alloying are the basis of the modern material science manufacturing. The design of the materials with expected required performance limits is the next conceptual step for the materials scientist. As results, to make this step, the problem of a precise material structure analyses on the microstructural level is one os the major importance for the next generation materials design. The complexity of the material structure across the scales(macro-micro) requires a new non deterministic methods for better understanding of the connectivity betwen a materials performance and material microstructure features. This work presents a various new research methodologies and techniques of the material microstructure characterization and numerical design with future applications to the anlyses of the material behavior. The focus of the particular research was to analyse a new cross correlation function of the material structure on the micro length scale and develop a novel framework which allows a better understanding of various important material phenomenas such as failure initiation and recrystallization.