New algorithmic developments in maximum consensus robust fitting

In many computer vision applications, the task of robustly estimating the set of parameters of a geometric model is a fundamental problem. Despite the longstanding research efforts on robust model fitting, there remains significant scope for investigation. For a large number of geometric estimation tasks in computer vision, maximum consensus is the most popular robust fitting criterion. This thesis makes several contributions in the algorithms for consensus maximization. Randomized hypothesize-and-verify algorithms are arguably the most widely used class of techniques for robust estimation thanks to their simplicity. Though efficient, these randomized heuristic methods do not guarantee finding good maximum consensus estimates. To improve the randomize algorithms, guided sampling approaches have been developed. These methods take advantage of additional domain information, such as descriptor matching scores, to guide the sampling process. Subsets of the data that are more likely to result in good estimates are prioritized for consideration. However, these guided sampling approaches are ineffective when good domain information is not available. This thesis tackles this shortcoming by proposing a new guided sampling algorithm, which is based on the class of LP-type problems and Monte Carlo Tree Search (MCTS). The proposed algorithm relies on a fundamental geometric arrangement of the data to guide the sampling process. Specifically, we take advantage of the underlying tree structure of the maximum consensus problem and apply MCTS to efficiently search the tree. Empirical results show that the new guided sampling strategy outperforms traditional randomized methods. Consensus maximization also plays a key role in robust point set registration. A special case is the registration of deformable shapes. If the surfaces have the same intrinsic shapes, their deformations can be described accurately by a conformal model. The uniformization theorem allows the shapes to be conformally mapped onto a canonical domain, wherein the shapes can be aligned using a M¨obius transformation. The problem of correspondence-free M¨obius alignment of two sets of noisy and partially overlapping point sets can be tackled as a maximum consensus problem. Solving for the M¨obius transformation can be approached by randomized voting-type methods which offers no guarantee of optimality. Local methods such as Iterative Closest Point can be applied, but with the assumption that a good initialization is given or these techniques may converge to a bad local minima. When a globally optimal solution is required, the literature has so far considered only brute-force search. This thesis contributes a new branch-and-bound algorithm that solves for the globally optimal M¨obius transformation much more efficiently. So far, the consensus maximization problems are approached mainly by randomized algorithms, which are efficient but offer no analytical convergence guarantee. On the other hand, there exist exact algorithms that can solve the problem up to global optimality. The global methods, however, are intractable in general due to the NP-hardness of the consensus maximization. To fill the gap between the two extremes, this thesis contributes two novel deterministic algorithms to approximately optimize the maximum consensus criterion. The first method is based on non-smooth penalization supported by a Frank-Wolfe-style optimization scheme, and another algorithm is based on Alternating Direction Method of Multipliers (ADMM). Both of the proposed methods are capable of handling the non-linear geometric residuals commonly used in computer vision. As will be demonstrated, our proposed methods consistently outperform other heuristics and approximate methods.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 201

Foreign parent control and international joint venture performance / : evidence from Finnish multinational firms

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Developing generic skills for students via extra-curricular activities in Vietnamese universities: Practices and influential factors

Developing generic skills (GS) for students has become central in many higher education curricula lately. However, there is still a lack of studies regarding how these skills are developed for students, especially those in developing countries. Drawing from a PhD study, this article reports the contribution of extra-curricular activities in developing GS for students in Vietnamese universities and analyses factors influencing the effectiveness of developing GS for students via these activities. A content analysis of relevant documents and 69 interviews with university leaders, academics and organisers of the Youth union and its associates (YUA) showed that extra-curricular activities were involved as an integral component of a university’s strategy for training students in GS. This was due to a lack of curriculum autonomy, which restricted most Vietnamese universities from adding skills subjects into the curriculum, and the YUA also had a long-standing tradition of developing non-discipline-specific skills for students. The YUA were found to successfully develop GS for students via extra-curricular activities; however, their operation was influenced by university leadership, student participation, external stakeholders’ support, and the leadership of the YUA. The article argues that extra-curricular activities were conducive to developing GS for students; therefore, they should be included in student skills development programs in higher education. &nbsp

Recent Advances in TiO2 Nanotube-Based Materials for Photocatalytic Applications Designed by Anodic Oxidation

This book chapter reports some spectacular and interesting 1D nanostructures of TiO2, which are grown by the anodic oxidation. Under suitable conditions, conventional one-step anodic oxidation is available to grow TiO2 nanotube arrays (TNAs) and TiO2 nanowires/nanotubes; meanwhile, two-step anodic oxidation allows fabricating some novel TNAs with spectacular morphologies such as highly ordered TNAs, bamboo-type TNAs, and lotus root-shaped TNAs. The formation mechanisms of these nanostructures during the anodic oxidation processes are elusive via studying effects of several key parameters such as oxidizing voltage, processing time, and electrolytes. In addition, the photocatalytic activity of the TNA-based nanomaterials is characterized by the degradation of pharmaceutical model, methylene blue, or the photoelectrochemical effect

Stabilization for equal-order polygonal finite element method for high fluid velocity and pressure gradient

This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method