Dichteoptimierung und Strukturanalyse von Hartkugelpackungen

Bei der Verwendung von Hartkugelpackungen als Modelle für verschiedene Systeme in Physik, Chemie und den Ingenieurwissenschaften kommen einige Fragen auf, z.B. nach dem Zusammenhang zwischen der Packungsdichte und der Radienverteilung der Kugeln bzw. der Packungsstruktur. Der erste Teil dieser Arbeit beschäftigt sich mit dem Problem der optimalen Packungsdichte von zufällig dichten Packungen. Es wird ein Optimierungsalgorithmus vorgestellt, der aus einer vorgegebenen Klasse von Radienverteilungen diejenige bestimmt, für die die Packungsdichte maximal wird. Die Packungsstruktur kann man durch verschiedene statistische Größen charakterisieren, die im zweiten Teil dieser Arbeit beschrieben werden. Dabei wird die Abhängigkeit dieser Größen von der Packungsdichte und der Radienverteilung untersucht und gezeigt, dass in monodispersen Packungen mit zunehmender Dichte erhebliche strukturelle Veränderungen auftreten: Im Dichteintervall zwischen 0,64 und 0,66 erfolgt offenbar ein Übergang von ungeordneten zu kristallinen Packungen, bei weiterer Verdichtung entwickelt sich schließlich eine FCC-Struktur

Minimax Estimation of Distances on a Surface and Minimax Manifold Learning in the Isometric-to-Convex Setting

We start by considering the problem of estimating intrinsic distances on a smooth surface. We show that sharper estimates can be obtained via a reconstruction of the surface, and discuss the use of the tangential Delaunay complex for that purpose. We further show that the resulting approximation rate is in fact optimal in an information-theoretic (minimax) sense. We then turn to manifold learning and argue that a variant of Isomap where the distances are instead computed on a reconstructed surface is minimax optimal for the problem of isometric manifold embedding

Factor Graphs for Computer Vision and Image Processing

Factor graphs have been used extensively in the decoding of error correcting codes such as turbo codes, and in signal processing. However, while computer vision and pattern recognition are awash with graphical model usage, it is some-what surprising that factor graphs are still somewhat under-researched in these communities. This is surprising because factor graphs naturally generalise both Markov random fields and Bayesian networks. Moreover, they are useful in modelling relationships between variables that are not necessarily probabilistic and allow for efficient marginalisation via a sum-product of probabilities. In this thesis, we present and illustrate the utility of factor graphs in the vision community through some of the field’s popular problems. The thesis does so with a particular focus on maximum a posteriori (MAP) inference in graphical structures with layers. To this end, we are able to break-down complex problems into factored representations and more computationally realisable constructions. Firstly, we present a sum-product framework that uses the explicit factorisation in local subgraphs from the partitioned factor graph of a layered structure to perform inference. This provides an efficient method to perform inference since exact inference is attainable in the resulting local subtrees. Secondly, we extend this framework to the entire graphical structure without partitioning, and discuss preliminary ways to combine outputs from a multilevel construction. Lastly, we further our endeavour to combine evidence from different methods through a simplicial spanning tree reparameterisation of the factor graph in a way that ensures consistency, to produce an ensembled and improved result. Throughout the thesis, the underlying feature we make use of is to enforce adjacency constraints using Delaunay triangulations computed by adding points dynamically, or using a convex hull algorithm. The adjacency relationships from Delaunay triangulations aid the factor graph approaches in this thesis to be both efficient and competitive for computer vision tasks. This is because of the low treewidth they provide in local subgraphs, as well as the reparameterised interpretation of the graph they form through the spanning tree of simplexes. While exact inference is known to be intractable for junction trees obtained from the loopy graphs in computer vision, in this thesis we are able to effect exact inference on our spanning tree of simplexes. More importantly, the approaches presented here are not restricted to the computer vision and image processing fields, but are extendable to more general applications that involve distributed computations

3D Non-Rigid Reconstruction with Prior Shape Constraints

3D non-rigid shape recovery from a single uncalibrated camera is a challenging, under-constrained problem in computer vision. Although tremendous progress has been achieved towards solving the problem, two main limitations still exist in most previous solutions. First, current methods focus on non-incremental solutions, that is, the algorithms require collection of all the measurement data before the reconstruction takes place. This methodology is inherently unsuitable for applications requiring real-time solutions. At the same time, most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. Note that specific types of shape variation might be governed by only a small number of parameters and therefore can be well-represented in a low dimensional manifold. The methods proposed in this thesis aim to estimate the non-rigid shapes and the corresponding camera trajectories, based on both the observations and the prior learned manifold. Firstly, an incremental approach is proposed for estimating the deformable objects. An important advantage of this method is the ability to reconstruct the 3D shape from a newly observed image and update the parameters in 3D shape space. However, this recursive method assumes the deformable shapes only have small variations from a mean shape, thus is still not feasible for objects subject to large scale deformations. To address this problem, a series of approaches are proposed, all based on non-linear manifold learning techniques. Such manifold is used as a shape prior, with the reconstructed shapes constrained to lie within the manifold. Those non-linear manifold based approaches significantly improve the quality of reconstructed results and are well-adapted to different types of shapes undergoing significant and complex deformations. Throughout the thesis, methods are validated quantitatively on 2D points sequences projected from the 3D motion capture data for a ground truth comparison, and are qualitatively demonstrated on real example of 2D video sequences. Comparisons are made for the proposed methods against several state-of-the-art techniques, with results shown for a variety of challenging deformable objects. Extensive experiments also demonstrate the robustness of the proposed algorithms with respect to measurement noise and missing data

Twin Related Domains in Polycrystalline Nickel-Base Superalloys: 3D Structure and Fatigue

Fatigue is the life limiting property of polycrystalline nickel-base superalloys used in turbine disks in the hot section of turbine engines for aerospace and power generation applications. Fabricating components through a powder metallurgy route provides enhanced properties and removes many extrinsic defects associated with fatigue crack formation, driving cracks to initiate at intrinsic microstructrual extremes. Fatigue cracks in the powder metallurgy alloy René 88DT frequently initiate in large grains that are in the tail of the size distribution and contain favorably oriented annealing twin boundaries. Fully characterizing twin boundaries and twin related domains requires 3D microstructural volumes. Datasets of 3D microstructure for René 88DT were collected by TriBeam tomography across a range of resolutions and volumes to characterize microstructure and annealing twins. Algorithms developed to analyze these datasets allow identification of strain localizing sites from 2D cross sections and the frequency of microstructural features amenable to fatigue crack initiation is statistically assessed. Twin related domain structure is quantified via network analysis and leveraged to propose a criterion for identifying fatal fatigue crack sites