RNA secondary structure is very important in many biological processes. Efficient structure prediction can give information for experimental investigations of these processes. Many available programs for secondary structure prediction exist. Some of them use single sequence, the others use more related sequences. Pseudoknots are still problematic for most methods. This work presents several methods and publicly available software and the implementation of minimum free energy method is described.

De nombreuses applications en vision par ordinateur comme le filtrage, la segmentation d'images, et la stéréovision peuvent être formulées comme des problèmes d'optimisation. Récemment les méthodes discrètes, convexes, globalement optimales ont reçu beaucoup d'attention. La méthode des "graph cuts'", très utilisée en vision par ordinateur est basée sur la résolution d'un problème de flot maximum discret, mais les solutions souffrent d'un effet de blocs,notamment en segmentation d'images. Une nouvelle formulation basée sur le problème continu est introduite dans le premier chapitre et permet d'éviter cet effet. La méthode de point interieur employée permet d'optimiser le problème plus rapidement que les méthodes existantes, et la convergence est garantie. Dans le second chapitre, la formulation proposée est efficacement étendue à la restauration d'image. Grâce à une approche du à la contrainte et à un algorithme proximal parallèle, la méthode permet de restaurer (débruiter, déflouter, fusionner) des images rapidement et préserve un meilleur contraste qu'avec la méthode de variation totale classique. Le chapitre suivant met en évidence l'existence de liens entre les méthodes de segmentation "graph-cuts'", le "randomwalker'', et les plus courts chemins avec un algorithme de segmentation par ligne de partage des eaux (LPE). Ces liens ont inspiré un nouvel algorithme de segmentation multi-labels rapide produisant une ligne de partage des eaux unique, moins sensible aux fuites que la LPE classique. Nous avons nommé cet algorithme "LPE puissance''. L'expression de la LPE sous forme d'un problème d'optimisation a ouvert la voie à de nombreuses applications possibles au delà de la segmentation d'images, par exemple dans le dernier chapitre en filtrage pour l'optimisation d'un problème non convexe, en stéréovision, et en reconstruction rapide de surfaces lisses délimitant des objets à partir de nuages de points bruités / Many computer vision applications such as image filtering, segmentation and stereovision can be formulated as optimization problems. Recently discrete, convex, globally optimal methods have received a lot of attention. Many graph-based methods suffer from metrication artefacts, segmented contours are blocky in areas where contour information is lacking. In the first part of this work, we develop a discrete yet isotropic energy minimization formulation for the continuous maximum flow problem that prevents metrication errors. This new convex formulation leads us to a provably globally optimal solution. The employed interior point method can optimize the problem faster than the existing continuous methods. The energy formulation is then adapted and extended to multi-label problems, and shows improvements over existing methods. Fast parallel proximal optimization tools have been tested and adapted for the optimization of this problem. In the second part of this work, we introduce a framework that generalizes several state-of-the-art graph-based segmentation algorithms, namely graph cuts, random walker, shortest paths, and watershed. This generalization allowed us to exhibit a new case, for which we developed a globally optimal optimization method, named "Power watershed''. Our proposed power watershed algorithm computes a unique global solution to multi labeling problems, and is very fast. We further generalize and extend the framework to applications beyond image segmentation, for example image filtering optimizing an L0 norm energy, stereovision and fast and smooth surface reconstruction from a noisy cloud of 3D points

Scene classification is performed on countless remote sensing images in support of operational activities. Automating this process is preferable since manual pixel-level classification is not feasible for large scenes. However, developing such an algorithmic solution is a challenging task due to both scene complexities and sensor limitations. The objective is to develop efficient and accurate unsupervised methods for classification (i.e., assigning each pixel to an appropriate generic class) and for labeling (i.e., properly assigning true labels to each class). Unique from traditional approaches, the proposed bootstrap approach achieves classification and labeling without training data. Here, the full image is partitioned into subimages and the true classes found in each subimage are provided by the user. After these steps, the rest of the process is automatic. Each subimage is individually classified into regions and then using the joint information from all subimages and regions the optimal configuration of labels is found based on an objective function based on a Markov random field (MRF) model. The bootstrap approach has been successfully demonstrated with SAR sea-ice and lake ice images which represent challenging scenes used operationally for ship navigation, climate study, and ice fraction estimation. Accuracy assessment is based on evaluation conducted by third party experts. The bootstrap method is also demonstrated using synthetic and natural images. The impact of this technique is a repeatable and accurate methodology that generates classified maps faster than the standard methodology.

This thesis explores synthesis by example as a paradigm for rendering real-world phenomena. In particular, phenomena that can be visually described as texture are considered. We exploit, for synthesis, the self-repeating nature of the visual elements constituting these texture exemplars. Techniques for unconstrained as well as constrained/controllable synthesis of both image and video textures are presented. For unconstrained synthesis, we present two robust techniques that can perform spatio-temporal extension, editing, and merging of image as well as video textures. In one of these techniques, large patches of input texture are automatically aligned and seamless stitched with each other to generate realistic looking images and videos. The second technique is based on iterative optimization of a global energy function that measures the quality of the synthesized texture with respect to the given input exemplar. We also present a technique for controllable texture synthesis. In particular, it allows for generation of motion-controlled texture animations that follow a specified flow field. Animations synthesized in this fashion maintain the structural properties like local shape, size, and orientation of the input texture even as they move according to the specified flow. We cast this problem into an optimization framework that tries to simultaneously satisfy the two (potentially competing) objectives of similarity to the input texture and consistency with the flow field. This optimization is a simple extension of the approach used for unconstrained texture synthesis. A general framework for example-based synthesis and rendering is also presented. This framework provides a design space for constructing example-based rendering algorithms. The goal of such algorithms would be to use texture exemplars to render animations for which certain behavioral characteristics need to be controlled. Our motion-controlled texture synthesis technique is an instantiation of this framework where the characteristic being controlled is motion represented as a flow field.

CO₂ injection in oil reservoirs provides the dual benefit of increasing oil recovery as well as sequestration. Compositional simulations using phase behavior calculations are used to model miscibility and estimate oil recovery. The injected CO₂, however, is known to react with brine. The precipitation and dissolution reactions, especially with carbonate rocks, can have undesirable consequences. The geochemical reactions can also change the mole numbers of components and impact the phase behavior of hydrocarbons. A Gibbs free energy framework that integrates phase equilibrium computations and geochemical reactions is presented in this dissertation. This framework uses the Gibbs free energy function to unify different phase descriptions - Equation of State (EOS) for hydrocarbon components and activity coefficient model for aqueous phase components. A Gibbs free energy minimization model was developed to obtain the equilibrium composition for a system with not just phase equilibrium (no reactions) but also phase and chemical equilibrium (with reactions). This model is adaptable to different reservoirs and can be incorporated in compositional simulators. The Gibbs free energy model is used for two batch calculation applications. In the first application, solubility models are developed for acid gases (CO₂ /H2 S) in water as well as brine at high pressures (0.1 - 80 MPa) and high temperatures (298-393 K). The solubility models are useful for formulating acid gas injection schemes to ensure continuous production from contaminated gas fields as well as for CO₂ sequestration. In the second application, the Gibbs free energy approach is used to predict the phase behavior of hydrocarbon mixtures - CO₂ -nC₁₄ H₃₀ and CH₄ -CO₂. The Gibbs free energy model is also used to predict the impact of geochemical reactions on the phase behavior of these two hydrocarbon mixtures. The Gibbs free energy model is integrated with flow using operator splitting to model an application of cation exchange reactions between aqueous phase and the solid surface. A 1-D numerical model to predict effluent concentration for a system with three cations using the Gibbs free energy minimization approach was observed to be faster than an equivalent stoichiometric approach. Analytical solutions were also developed for this system using the hyperbolic theory of conservation laws and are compared with experimental results available at laboratory and field scales. / text

Many computer vision applications such as image filtering, segmentation and stereovision can be formulated as optimization problems. Recently discrete, convex, globally optimal methods have received a lot of attention. Many graph-based methods suffer from metrication artefacts, segmented contours are blocky in areas where contour information is lacking. In the first part of this work, we develop a discrete yet isotropic energy minimization formulation for the continuous maximum flow problem that prevents metrication errors. This new convex formulation leads us to a provably globally optimal solution. The employed interior point method can optimize the problem faster than the existing continuous methods. The energy formulation is then adapted and extended to multi-label problems, and shows improvements over existing methods. Fast parallel proximal optimization tools have been tested and adapted for the optimization of this problem. In the second part of this work, we introduce a framework that generalizes several state-of-the-art graph-based segmentation algorithms, namely graph cuts, random walker, shortest paths, and watershed. This generalization allowed us to exhibit a new case, for which we developed a globally optimal optimization method, named "Power watershed''. Our proposed power watershed algorithm computes a unique global solution to multi labeling problems, and is very fast. We further generalize and extend the framework to applications beyond image segmentation, for example image filtering optimizing an L0 norm energy, stereovision and fast and smooth surface reconstruction from a noisy cloud of 3D points

Scene classification is performed on countless remote sensing images in support of operational activities. Automating this process is preferable since manual pixel-level classification is not feasible for large scenes. However, developing such an algorithmic solution is a challenging task due to both scene complexities and sensor limitations. The objective is to develop efficient and accurate unsupervised methods for classification (i.e., assigning each pixel to an appropriate generic class) and for labeling (i.e., properly assigning true labels to each class). Unique from traditional approaches, the proposed bootstrap approach achieves classification and labeling without training data. Here, the full image is partitioned into subimages and the true classes found in each subimage are provided by the user. After these steps, the rest of the process is automatic. Each subimage is individually classified into regions and then using the joint information from all subimages and regions the optimal configuration of labels is found based on an objective function based on a Markov random field (MRF) model. The bootstrap approach has been successfully demonstrated with SAR sea-ice and lake ice images which represent challenging scenes used operationally for ship navigation, climate study, and ice fraction estimation. Accuracy assessment is based on evaluation conducted by third party experts. The bootstrap method is also demonstrated using synthetic and natural images. The impact of this technique is a repeatable and accurate methodology that generates classified maps faster than the standard methodology.

With the advance of the LCD technology, video quality is becoming increasingly important. In this thesis, we develop hardware-friendly low-complexity enhancement algorithms. Video quality enhancement methods can be classified into two main categories. Single frame methods are the first category. These methods have generally low computational complexity. Multi-frame methods combine information from more than one frame and require the motion information of objects in the scene to do so. We first concentrate on the contrast-enhancement problem by using both global (frame-wise) and local information derived from the image. We use the image histogram and present a regularization-based histogram modification method to avoid problems that are often created by histogram equalization. Next, we design a compression artifact reduction algorithm that reduces ringing artifacts, which is disturbing especially on large displays. Furthermore, to remove the blurriness in the original video we present a non-iterative diffusion-based sharpening algorithm, which enhances edges in a ringing-aware fashion. The diffusion-based technique works on gradient approximations in a neighborhood individually. This gives more freedom compared to modulating the high-pass filter output that is used to sharpen the edges. Motion estimation enables applications such as motion-compensated noise reduction, frame-rate conversion, de-interlacing, compression, and super-resolution. Motion estimation is an ill-posed problem and therefore requires the use of prior knowledge on motion of objects. Objects have inertia and are usually larger then pixels or a block of pixels in size, which creates spatio-temporal correlation. We design a method that uses temporal redundancy to improve motion-vector search by choosing bias vectors from the previous frame and adaptively penalizes deviations from the bias vectors. This increases the robustness of the motion-vector search. The spatial correlation is more reliable because temporal correlation is difficult to use when the objects move fast or accelerate in time, or have small sizes. Spatial smoothness is not valid across motion boundaries. We investigate using energy minimization for motion estimation and incorporate the spatial smoothness prior into the energy. By formulating the energy minimization iterations for each motion vector as the primal problem, we show that the dual problem is motion segmentation for that specific motion vector.

Made available in DSpace on 2016-12-12T20:15:50Z (GMT). No. of bitstreams: 1 Jorge Batista - resumo.pdf: 37444 bytes, checksum: 8f8f7a4b6f5f96a5fc6085e5128c66c2 (MD5) Previous issue date: 2013-03-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The control of surface wettability is an issue of great scientific interest because of the large number of applications both as hydrophobic to hydrophilic surfaces, such as medical instruments, fluidic microdevices, coating for microdevices, manipulation of nanoparticles, microscale motors, lubricants, waterproofing surfaces and even instruments for domestic use. The objectives of this work are to compare the values obtained in experiments with surfaces of well-defined topography with available theoretical models and characterize the transition between hydrophobic and superhydrophobic surfaces with the proposition of configurations and criteria that lead to a better understanding of the wettability and superhydrophobic surfaces production, in the light of the energy minimization. / O controle da molhabilidade de superfícies é um tema de grande interesse científico em função da grande quantidade de aplicações, tanto para superfícies hidrofílicas quanto hidrofóbicas, como é o caso de instrumentos médicos, microdispositivos fluídicos, recobrimento para microdispositivos, manipulação de nanopartículas, motores em microescala, lubrificantes, impermeabilização de superfícies e mesmo em instrumentos de uso doméstico. Os objetivos deste trabalho são comparar os valores obtidos em experimentos com superfícies de topografia bem definida com os modelos teóricos disponíveis e caracterizar a transição entre superfícies hidrofóbicas e superhidrofóbicas, com a proposição de configurações e critérios que levem a um melhor entendimento da molhabilidade e da produção de superfícies superhidrofóbicas, sob a luz da minimização de energia.

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