Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

On peut atteindre des images réalistes par la simulation du transport lumineuse avec des méthodes de Monte-Carlo. La possibilité d’utiliser des sources de lumière réalistes pour synthétiser les images contribue grandement à leur réalisme physique. Parmi les modèles existants, ceux basés sur des cartes d’environnement ou des champs lumineuse sont attrayants en raison de leur capacité à capter fidèlement les effets de champs lointain et de champs proche, aussi bien que leur possibilité d’être acquis directement. Parce que ces sources lumineuses acquises ont des fréquences arbitraires et sont éventuellement de grande dimension (4D), leur utilisation pour un rendu réaliste conduit à des problèmes de performance.Dans ce manuscrit, je me concentre sur la façon d’équilibrer la précision de la représentation et de l’efficacité de la simulation. Mon travail repose sur la génération des échantillons de haute qualité à partir des sources de lumière par des estimateurs de Monte-Carlo non-biaisés. Dans ce manuscrit, nous présentons trois nouvelles méthodes.La première consiste à générer des échantillons de haute qualité de manière efficace à partir de cartes d’environnement dynamiques (i.e. qui changent au cours du temps). Nous y parvenons en adoptant une approche GPU qui génère des échantillons de lumière grâce à une approximation du facteur de forme et qui combine ces échantillons avec ceux issus de la BRDF pour chaque pixel d’une image. Notre méthode est précise et efficace. En effet, avec seulement 256 échantillons par pixel, nous obtenons des résultats de haute qualité en temps réel pour une résolution de 1024 × 768. La seconde est une stratégie d’échantillonnage adaptatif pour des sources représente comme un "light field". Nous générons des échantillons de haute qualité de manière efficace en limitant de manière conservative la zone d’échantillonnage sans réduire la précision. Avec une mise en oeuvre sur GPU et sans aucun calcul de visibilité, nous obtenons des résultats de haute qualité avec 200 échantillons pour chaque pixel, en temps réel et pour une résolution de 1024×768. Le rendu est encore être interactif, tant que la visibilité est calculée en utilisant notre nouvelle technique de carte d’ombre (shadow map). Nous proposons également une approche totalement non-biaisée en remplaçant le test de visibilité avec une approche CPU. Parce que l’échantillonnage d’importance à base de lumière n’est pas très efficace lorsque le matériau sous-jacent de la géométrie est spéculaire, nous introduisons une nouvelle technique d’équilibrage pour de l’échantillonnage multiple (Multiple Importance Sampling). Cela nous permet de combiner d’autres techniques d’échantillonnage avec le notre basé sur la lumière. En minimisant la variance selon une approximation de second ordre, nous sommes en mesure de trouver une bonne représentation entre les différentes techniques d’échantillonnage sans aucune connaissance préalable. Notre méthode est pertinence, puisque nous réduisons effectivement en moyenne la variance pour toutes nos scènes de test avec différentes sources de lumière, complexités de visibilité et de matériaux. Notre méthode est aussi efficace par le fait que le surcoût de notre approche «boîte noire» est constant et représente 1% du processus de rendu dans son ensemble. / Realistic images can be rendered by simulating light transport with Monte Carlo techniques. The possibility to use realistic light sources for synthesizing images greatly contributes to their physical realism. Among existing models, the ones based on environment maps and light fields are attractive due to their ability to capture faithfully the far-field and near-field effects as well as their possibility of being acquired directly. Since acquired light sources have arbitrary frequencies and possibly high dimension (4D), using such light sources for realistic rendering leads to performance problems.In this thesis, we focus on how to balance the accuracy of the representation and the efficiency of the simulation. Our work relies on generating high quality samples from the input light sources for unbiased Monte Carlo estimation. In this thesis, we introduce three novel methods.The first one is to generate high quality samples efficiently from dynamic environment maps that are changing over time. We achieve this by introducing a GPU approach that generates light samples according to an approximation of the form factor and combines the samples from BRDF sampling for each pixel of a frame. Our method is accurate and efficient. Indeed, with only 256 samples per pixel, we achieve high quality results in real time at 1024 × 768 resolution. The second one is an adaptive sampling strategy for light field light sources (4D), we generate high quality samples efficiently by restricting conservatively the sampling area without reducing accuracy. With a GPU implementation and without any visibility computations, we achieve high quality results with 200 samples per pixel in real time at 1024 × 768 resolution. The performance is still interactive as long as the visibility is computed using our shadow map technique. We also provide a fully unbiased approach by replacing the visibility test with a offline CPU approach. Since light-based importance sampling is not very effective when the underlying material of the geometry is specular, we introduce a new balancing technique for Multiple Importance Sampling. This allows us to combine other sampling techniques with our light-based importance sampling. By minimizing the variance based on a second-order approximation, we are able to find good balancing between different sampling techniques without any prior knowledge. Our method is effective, since we actually reduce in average the variance for all of our test scenes with different light sources, visibility complexity, and materials. Our method is also efficient, by the fact that the overhead of our "black-box" approach is constant and represents 1% of the whole rendering process.

Les études menées dans cette thèse portent sur le rôle des visages dans l'attention visuelle. Nous avons cherché à mieux comprendre l'influence des visages dans les vidéos sur les mouvements oculaires, afin de proposer un modèle de saillance visuelle pour la prédiction de la direction du regard. Pour cela, nous avons analysé l'effet des visages sur les fixations oculaires d'observateurs regardant librement (sans consigne ni tâche particulière) des vidéos. Nous avons étudié l'impact du nombre de visages, de leur emplacement et de leur taille. Il est apparu clairement que les visages dans une scène dynamique (à l'instar de ce qui se passe sur les images fixes) modifie fortement les mouvements oculaires. En nous appuyant sur ces résultats, nous avons proposé un modèle de saillance visuelle, qui combine des caractéristiques classiques de bas-niveau (orientations et fréquences spatiales, amplitude du mouvement des objets) avec cette caractéristique importante de plus haut-niveau que constitue les visages. Enfin, afin de permettre des traitements plus proches du temps réel, nous avons développé une implémentation parallèle de ce modèle de saillance visuelle sur une plateforme multi-GPU. Le gain en vitesse est d'environ 130x par rapport à une implémentation sur un processeur multithread.

La croissance exponentielle de la génération de données pour la bioinformatique couplée à une stagnation des fréquences d'horloge des processeurs modernes accentuent la nécessité de fournir des implémentation tirant bénéfice des capacités parallèles des ordinateurs modernes. Cette thèse se concentre sur des algorithmes et implementations pour des problèmes de bioinformatique. Plusieurs types de parallélisme sont décrits et exploités. Cette thèse présente des applications en génétique, avec un outil de détection de QTL paralllisé sur GPU, en comparaison de structures de protéines, avec un outil permettant de trouver des régions similaires entre protéines parallélisé sur CPU, ainsi qu'à l'analyse de larges graphes avec une implémentation multi-GPUs d'un nouvel algorithme pour le problème du "All-Pairs Shortest Path".

The work described in this thesis is part of the Open Space project, a collaboration between Linköping University, NASA and the American Museum of Natural History. The long-term goal of Open Space is a multi-purpose, open-source scientific visualization software. The thesis covers the research and implementation of a pipeline for preparing and rendering volumetric data. The developed pipeline consists of three stages: A data formatting stage which takes data from various sources and prepares it for the rest of the pipeline, a pre-processing stage which builds a tree structure of of the raw data, and finally an interactive rendering stage which draws a volume using ray-casting. The pipeline is a fully working proof-of-concept for future development of Open Space, and can be used as-is to render space weather data using a combination of suitable data structures and an efficient data transfer pipeline. Many concepts and ideas from this work can be utilized in the larger-scale software project.

This master´s thesis deals with realization of supercomputer using graphic card with CUDA technology. The theoretical part of this thesis describes the function and the possibility of graphic cards and desktop computers and processes taking place in the proces sof calculations on them. The practical part deals with creation system for calculations on the graphic card using the algorithm of artificial intelligence, more specifically artificial neural networks. Subsequently is the generated program used for data classification of large input data file. Finally the results are compared.

Intel Larrabee is one of the first of fully programmable graphical architectures. Thesis describes this many-core architecture by hardware implementation and programmer's model point of view. Larrabee bets on many complete in-order cores, built over x86 instruction set. Cores contains four hardware threads, each with it's own register file, and new vector processing unit. Vector processing unit together with instruction set extension rapidly increases system performance. New cache modes helps to increase throughput even when irregular data structures. This architecture is not focused only on computer graphics nor image processing, but all parallel tasks. Second part of this text deals with hologram synthesis. Specifically, it brings two new methods for patch of point light sources generation with concrete radiation.

The aim of this thesis is to research how the functional paradigm can be used for hardware acceleration with an emphasis on data-parallel tasks. The level of abstraction of the traditional hardware description languages, such as VHDL or Verilog, is becoming to low. High-level languages from the domains of software development and modeling, such as C/C++, SystemC or MATLAB, are experiencing a boom for hardware description on the algorithmic or behavioral level. Functional Languages are not so commonly used, but they outperform imperative languages in verification, the ability to capture inherent paralellism and the compactness of code. Data-parallel task are often accelerated on FPGAs, GPUs and multicore processors. In this thesis, we use a library for general-purpose GPU programs called Accelerate and extend it to produce VHDL. Accelerate is a domain-specific language embedded into Haskell with a backend for the NVIDIA CUDA platform. We use the language and its frontend, and create a new backend for high-level synthesis of circuits in VHDL.

Thesis deals with discrete optimization problems. It focusses on faster ways to find good solutions by means of heuristics and parallel processing. Based on ant colony optimization (ACO) algorithm coupled with k-optimization local search approach, it aims at massively parallel computing on graphics processors provided by Nvidia CUDA platform. Well-known travelling salesman problem (TSP) is used as a case study. Solution is based on dividing task into subproblems using tour-based partitioning, parallel processing of distinct parts and their consecutive recombination. Provided parallel code can perform computation more than seventeen times faster than the sequential version.

Raytracing is a basic technique for displaying 3D objects. The goal of this thesis is to demonstrate the possibility of implementing raytracer using a programmable GPU. The algorithm and its modified version, implemented using "C for CUDA" language, are described. The raytracer is focused on displaying dynamic scenes. For this purpose the KD tree structure, bounding volume hierarchies and PBO transfer are used. To achieve realistic output, photon mapping was implemented.