Browsing by Subject "Principal Component Analysis (PCA)"

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Open Access
Memory-efficient constrained delaunay tetrahedralization of large three-dimensional triangular meshes
(2022-07) Erkoç, Ziya
We propose a divide-and-conquer algorithm that can solve the Constrained De-launay Tetrahedralization (CDT) problem. It consists of three stages: Input Partitioning, Surface Closure, and Merge. We first partition the input into sev-eral pieces to reduce the problem size. We apply 2D Triangulation to close the open boundaries to make new pieces watertight. Each piece is then sent to Tet-Gen [Hang Si, “TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator”, ACM Transactions on Mathematical Software, Vol. 41, No. 2, Article No. 11, 36 pages, January 2015] for processing. We finally merge each tetrahedral mesh to calculate the final solution. In addition, we apply post-processing to remove vertices we introduced during the input partitioning stage to preserve the in-put triangles. An alternative approach that does not insert new vertices and eliminates the need for post-processing is also possible but not robust. The benefit of our method is that it can reduce memory usage or increase the speed of the process. It can even tetrahedralize meshes that TetGen cannot do due to the memory’s insufficiency. We also observe that this method can increase the overall tetrahedral mesh quality.
Open Access
Methods for target detection in SAR images
(2009) Duman, Kaan
Automatic recognition and classification of man-made objects in SAR (Synthetic Aperture Radar) images have been an active research area because SAR sensors can produce images of scenes in all weather conditions at any time of the day which is not possible with infrared and optical sensors [1, 2]. In this thesis, different feature parameter extraction methods from SAR images are proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix of a ROI (region of interest). Entries of the covariance matrix are used in target detection. In addition, the use of computationally more efficient region codifference matrix for target detection in SAR images is also introduced. Simulation results of target detection in MSTAR (Moving and Stationary Target Recognition) database are presented. The RC and region codifference methods deliver high detection accuracies and low false alarm rates. The performance of these methods is investigated with various distance metrics and Support Vector Machine (SVM) classifiers. It is also observed that the region codifference method produces better results than the commonly used Principle Component Analysis (PCA) method which is used together with SVM. The second part of the thesis offers some techniques to decrease the computational cost of the proposed methods. In this approach, ROIs are filtered by directional filters (DFs) at first as a pre-processing stage. Images are categorized according to the filter outputs. The proposed RC and codifference methods are applied within the categories determined by these filters. Simulation results of target detection in MSTAR database are presented through decisions made with l1 norm distance metric and SVM. The number of comparisons made with the training images using l1 norm distance measure decreases as these images are distributed into categories. Therefore, the computational cost of the previous algorithm is significantly reduced. SAR image classification results based on l1 norm distance metric are better than the results obtained using SVM and they show that the two-stage approach does not reduce the performance rate of the previously proposed method much, especially when codifference features are used.