搜索资源列表
3dActive_src
- 一个3d可视化控件 ，不需要在进行编译 遵循Cpol-The control can perform the following functions: Axis customization, including customizable font, colors, and titles. Plot a large number of points and updating one or more plots on the graph with new data, replacin
bellman-ford.c
- bellman-ford algorithm to find shortest distance in a graph. useful for path planning.
Snake-model-bsegmentation-method
- 改进的T- Snake 算法首先在分水岭法中, 对相邻区域以其像素数、灰度均值和灰度方差定义距离, 并据其在图像上建立新的连通图, 以对图像过度分割而产生的一些过小区域合并 其次, 在模型跨边缘时, 利用已分割断层图像中模型内部区域的统计特征, 用区域生长法获取内点并重新参数化模型, 使模型不再跨边缘, 以保证模型形变到正确的边缘-Improved T-Snake algorithm first law in the watershed, the number of pixels in its
graph
- to calculate the maximum distance between n number of points in a graph
tulun
- 图论算法与代码，求一个城市到另一城市最短距离-Graph theory algorithms and code, find the shortest distance of a city to another city
dijkstra_openmp
- DIJKSTRA_OPENMP is a C++ program which illustrates the use of the OpenMP application program interface by implementing Dijkstra s minimum graph distance algorithm
Find-Center-of-Mass-and-Distance
- 计算质心坐标(c)与边界与质心间的距离r(i)，并绘制theta 与r_histogram 的关系曲线图-Calculating the centroid coordinates (c) between the boundary and the centroid distance r (i), and the mapping relationship between theta and r_histogram graph
solve_show_value_1
- 基于RSSI的三点定位测算距离，并将坐标显示在图形中-Three RSSI-based localization estimates the distance, and the coordinates are displayed in the graph
smith_matlabcode
- 自己改编的代码，可以进行基本参量的计算（归一化阻抗和导纳，驻波比、行波系数和反射系数等），鼠标取点绘制并计算相关特性阻抗，计算距离负载d处的输入阻抗，单枝节匹配和双枝节匹配，圆图的绘制、保存图像等。且整个过程可视化~-Own adaptation of code can be calculated basic parameters (normalized impedance and admittance, VSWR, traveling wave and reflection coefficie
histram
- 用于相似颜色图像检索，传统直方图法，巴氏系数法，传统欧式距离法-For the image similarity graph, the traditional histogram Bhattacharyya coefficient method, traditional Euclidean distance method
LEM-Algorithm
- LEM（拉普拉斯特征映射）算法，拉普拉斯特征映射是基于局部邻域，保持局部结构的流形学习方法。LEM通过一个无向加权图刻画流形上数据点间的近邻关系，图的顶点为原始数据点，图的边对应点之间的近邻关系，边的权值对应近邻点之间的相似程度（也可以是某种距离），LEM在低维嵌入空间中尽量保持图中数据点之间的近邻关系，然后求取嵌入坐标。通俗的说，LEM认为在高维数据空间离得近的点在低维嵌入空间也应该离得近-LEM (Laplace feature mapping) algorithm, Laplace fea