面向自适应计算的局部四面体网格重划分

为有效满足h自适应的网格重划分要求, 提出通过前沿推进法和Delaunay算法对四面体网格进行局部重划分. 首先, 在重划分过程中， 采用由线到面、 由面到体的顺序保证整体网格的协调性; 其次, 通过局部尺寸函数保证网格尺寸平滑过渡; 最后, 用投影法使网格满足几何保形. 仿真实验结果表明, 该算法适用于包含多部件的复杂计算机辅助设计(CAD)模型, 在h自适应加密过程中网格更贴近真实几何形态, 且重划分后可保证网格单元的质量.

Fast Image Feature Region Detection Based on Scale Invariant Feature Transformation

Aiming at the problem that the fast image feature region detection was affected by noise and scale space, which resulted in low detection accuracy, large delay and unreliable detection results, the author proposed a fast image feature region detection method based on scale invariant feature transformation. Each pixel in the image was weighted and smoothed by weighted kernel function to realize image denoising. On this basis, image feature points were determined by constructing image Gaussian scale space, low contrast pixels and edge pixels were deleted, and image feature points were quickly extracted and the detection of location of feature points was the image feature region. The simulation results show that the method can achieve fast and comprehensive detection of image feature region with high efficiency and precision.