Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph...Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed with its guidance function. During the refinement phase, the vertex gain is exploited as ant＇s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute （ANSI） C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks.展开更多
针对当前图像配准算法配准时间过长、配准正确率低等问题,提出一种基于网格运动统计(GMS)、矢量系数相似度(VCS)与图割随机抽样一致性(GC-RANSAC)的图像配准算法。首先,通过ORB(Oriented FAST and Rotated BRIEF)算法对图像进行特征点提...针对当前图像配准算法配准时间过长、配准正确率低等问题,提出一种基于网格运动统计(GMS)、矢量系数相似度(VCS)与图割随机抽样一致性(GC-RANSAC)的图像配准算法。首先,通过ORB(Oriented FAST and Rotated BRIEF)算法对图像进行特征点提取,并对特征点进行暴力匹配。之后,通过GMS算法对图像中的粗匹配特征点进行网格划分,利用网格中正确匹配点邻域内具有较高特征支持量的原理对粗匹配对进行筛选;并引入图像匹配对在进行矢量运算时VCS不超过某一设定阈值的原理对匹配对进行部分剔除,以利于算法后期的快速收敛。最后,运用GCRANSAC算法进行局部最优模型拟合,得到精匹配特征点集,实现高精度的图像配准和拼接。通过与ASIFT+RANSAC、GMS、AKAZE+RANSAC、GMS+GC-RANSAC等算法对比,实验结果表明,该算法在平均匹配精度上提高了30.34%,平均匹配时间缩短0.54 s。展开更多
We consider the reconstruction of shared secrets in communication networks, which are modelled by graphs whose components are subject to possible failure. The reconstruction probability can be approximated using minim...We consider the reconstruction of shared secrets in communication networks, which are modelled by graphs whose components are subject to possible failure. The reconstruction probability can be approximated using minimal cuts, if the failure probabilities of vertices and edges are close to zero. As the main contribution of this paper, node separators are used to design a heuristic for the near-optimal placement of secrets sets on the vertices of the graph.展开更多
基金the International Cooperation Project of Ministry of Science and Technology of P. R. China (GrantNo.CB7-2-01)SEC E-Institute: Shanghai High Institutions Grid
文摘Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed with its guidance function. During the refinement phase, the vertex gain is exploited as ant＇s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute （ANSI） C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks.
文摘针对当前图像配准算法配准时间过长、配准正确率低等问题,提出一种基于网格运动统计(GMS)、矢量系数相似度(VCS)与图割随机抽样一致性(GC-RANSAC)的图像配准算法。首先,通过ORB(Oriented FAST and Rotated BRIEF)算法对图像进行特征点提取,并对特征点进行暴力匹配。之后,通过GMS算法对图像中的粗匹配特征点进行网格划分,利用网格中正确匹配点邻域内具有较高特征支持量的原理对粗匹配对进行筛选;并引入图像匹配对在进行矢量运算时VCS不超过某一设定阈值的原理对匹配对进行部分剔除,以利于算法后期的快速收敛。最后,运用GCRANSAC算法进行局部最优模型拟合,得到精匹配特征点集,实现高精度的图像配准和拼接。通过与ASIFT+RANSAC、GMS、AKAZE+RANSAC、GMS+GC-RANSAC等算法对比,实验结果表明,该算法在平均匹配精度上提高了30.34%,平均匹配时间缩短0.54 s。
文摘We consider the reconstruction of shared secrets in communication networks, which are modelled by graphs whose components are subject to possible failure. The reconstruction probability can be approximated using minimal cuts, if the failure probabilities of vertices and edges are close to zero. As the main contribution of this paper, node separators are used to design a heuristic for the near-optimal placement of secrets sets on the vertices of the graph.
