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Maximum likelihood estimators for circular structural modelZaeva, Maria. January 2009 (has links) (PDF)
Thesis (M.S.)--University of Alabama at Birmingham, 2009. / Title from PDF title page (viewed Jan. 21, 2010). Additional advisors: Yulia Karpeshina, Ian Knowles, Rudi Weikard. Includes bibliographical references (p. 19).
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THE SINGULAR POINTS OF THE FUNDAMENTAL DOMAINS FOR THE GROUPS OF BIANCHIWoodruff, William Munger, 1936- January 1967 (has links)
No description available.
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Iterative processes generating dense point setsAmbrus, Gergely, Bezdek, András, January 2006 (has links) (PDF)
Thesis(M.S.)--Auburn University, 2006. / Abstract (p.34-35). Vita. Includes bibliographic references.
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Invariant sets near the collinear Langrangian points of the nonplanear restricted three-body problemAppleyard, David F. January 1970 (has links)
Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.
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General discriminative optimization for point set registrationZhao, Y., Tang, W., Feng, J., Wan, Tao Ruan, Xi, L. 26 March 2022 (has links)
Yes / Point set registration has been actively studied in computer vision and graphics. Optimization algorithms are at the core of solving registration problems. Traditional optimization approaches are mainly based on the gradient of objective functions. The derivation of objective functions makes it challenging to find optimal solutions for complex optimization models, especially for those applications where accuracy is critical. Learning-based optimization is a novel approach to address this problem, which learns the gradient direction from datasets. However, many learning-based optimization algorithms learn gradient directions via a single feature extracted from the dataset, which will cause the updating direction to be vulnerable to perturbations around the data, thus falling into a bad stationary point. This paper proposes the General Discriminative Optimization (GDO) method that updates a gradient path automatically through the trade-off among contributions of different features on updating gradients. We illustrate the benefits of GDO with tasks of 3D point set registrations and show that GDO outperforms the state-of-the-art registration methods in terms of accuracy and robustness to perturbations.
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Surface reconstruction using gamma shapesSun, Ying. January 2006 (has links) (PDF)
Thesis (Ph. D.)--University of Alabama at Birmingham, 2006. / Description based on contents viewed Jan. 26, 2007; title from title screen. Includes bibliographical references (p. 119-125).
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Machine Learning for LiDAR-SLAM : In Forest TerrainsHjert, Anton January 2021 (has links)
Point set registration is a well-researched yet still not a very exploited area in computer vision. As the field of machine learning grows, the possibilities of application expand. This thesis investigates the possibility to expand an already implemented probabilistic machine learning approach to point set registration to more complex, larger datasets gathered in a forest environment. The system used as a starting point was created by Järemo Lawin et. al. [10]. The aim of the thesis was to investigate the possibility to register the forest data with the existing system, without ground-truth poses, with different optimizers, and to implement a SLAM pipeline. Also, older methods were used as a benchmark for evaluation, more specifically iterative closest point(ICP) and fast global registration(FGR).To enable the gathered data to be processed by the registration algorithms, preprocessing was required. Transforming the data points from the coordinate system of the sensor to world relative coordinates via LiDAR base coordinates. Subsequently, the registration was performed with different approaches. Both the KITTI odometry dataset, which RLLReg originally was evaluated with[10], and the gathered forest data were used. Data augmentation was utilized to enable ground-truth-independent training and to increase diversity in the data. In addition, the registration results were used to create a SLAM-pipeline, enabling mapping and localization in the scanned areas. The results showed great potential for using RLLReg to register forest scenes compared to other, older, approaches. Especially, the lack of ground-truth was manageable using data augmentation to create training data. Moreover, there was no evidence that AdaBound improves the system when replacing the Adam-optimizer. Finally, forest models with sensor paths plotted were generated with decent results. However, a potential for post-processing with further refinement is possible. Nevertheless, the possibility of point set registration and LiDAR-SLAM using machine learning has been confirmed.
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3D Mapping of Islamic Geometric MotifsSayed, Zahra January 2017 (has links)
In this thesis a novel approach in generating 3D IGP is applied using shape grammar, an effective pattern generation method. The particular emphasis here is to generate the motifs (repeat unit) in 3D using parameterization, which can then be manipulated within 3D space to construct architectural structures. Three unique distinctive shape grammar algorithms were developed in 3D; Parameterized Shape Grammar (PSG), Auto-Parameterized Shape Grammar (APSG) and Volumetric Shell Shape Grammar (VSSG).
Firstly, the PSG generates the motifs in 3D. It allows one to use a single changeable regular 3D polygon, and forms a motif by given grammar rules including, Euclidean transformations and Boolean operations. Next, APSG was used to construct the architectural structures that manipulates the motif by automating the grammar rules. The APSG forms a wall, a column, a self-similarity star and a dome, the main features of Islamic architecture. However, applying Euclidean transformations to create non-Euclidean surfaces resulted in gaps and or overlaps which does not form a perfect tessellation. This is improved upon by the VSSM, which integrates two key methods, shell mapping and coherent point drift, to map an aesthetically accurate 3D IGM on a given surface.
This work has successfully presented methods for creating complex intricate 3D Islamic Geometric Motifs (IGM), and provided an efficient mapping technique to form visually appealing decorated structures. / Partially funded by the Centre of Visual Computing (CVC)
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Optimal Feature Selection for Spatial Histogram ClassifiersThapa, Mandira January 2017 (has links)
No description available.
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Tent-maps, two-point sets, and the self-Tietze propertyDavies, Gareth January 2011 (has links)
This thesis discusses three distinct topics. A topological space X is said to be self- Tietze if for every closed C eX, every continuous f: C -+ X admits a continuous extension F: X -+ X. We show that every disconnected, self- Tietze space is ultranormal. The Tychonoff Plank is an example of a compact self- Tietze space which is not completely normal, and we establish that a completely normal, zero- dimensional, homogeneous space need not be self- Tietze. A subset of the plane is a two-point set if it meets every straight line in exactly two points. We show that a two-point set cannot contain a dense G8 subset of an arc. We also show that the complement of a two-point set is necessarily path-connected. Finally, we construct a zero-dimensional subset of the plane of which the complement is simply-connected. For A E lR, the tent-map with slope A is the function f: [0, 1] -+ lR such that f(x) = AX for x :=:; ~ and f(x) = A(l - x) for x ~ ~. Properties of w-limit sets of tent-maps, i.e. sets of the form n {fn+k(x) I kEN} nEN for x E [0,1], are examined, and an example of a tent-map and a closed, invariant, nonempty, internally chain transitive subset of [0, 1] which is not an w-limit set is given.
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