搜索资源列表
NIPS2005_0433
- k-d tree 算法介绍，介绍了一种可行的方法，从而实现搜素。
KD_Tree
- KD_Tree In computer science, a kd-tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. kd-trees are a useful data structure for several applications, such as searches involving a m
libkdtree_0.7.0
- 一个KD-tree的类STL模板库，版本为0.7.0-libkdtree++ is an STL-like C++ template container implementation of k-dimensional space sorting, using a kd-tree.
kdtree
- In computer science, a kd-tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. kd-trees are a useful data structure for several applications, such as searches involving a multidime
knn
- 实现构建k-d树并且实现k-d树上的最邻近查找算法-Build a kd-tree and kd tree nearest neighbor search algorithm
kdtree-master
- K-d tree implementation in C++ for NN and KNN search Templated k-d tree example that makes use of boost geometry point classes. The k-d tree is build in bulk and supports N dimensions. The implementation contains a recursive and iterative ne
icp3d
- 基于K-D tree的ICP算法的C代码，可以用于点云匹配。代码有注释。简单易懂。适合初学者研究学习。-ICP algorithm based KD tree C code that can be used to match point cloud. Code annotated. Easy to understand. Suitable for beginners to learn.
KNN
- 基于k-d树的knn算法实现，实现较简单，供初学参考。-Knn, K-D tree
knn-kdtree
- kd树，分割k维数据空间的数据结构。主要应用于多维空间关键数据的搜索（如：范围搜索和最近邻搜索）。K-D树是二进制空间分割树的特殊的情况。-KD tree, the data structure of K dimensional data space. It is mainly used in the search of key data in multidimensional space (such as range search and nearest neighbor search). K
K d tree in matlab
- k d clustering in matlab
3-18有向树k值
- 要求有向树Ｔ的k个顶点组成的集合Ｆ，使cost(F)=∑min w(x)*d(x,u)的值达到最小。在一般情况下，有向树是一棵多叉树，为了便于计算，我们把多叉树变转成为与之等价的二叉树，将每个顶点的第一个儿子做为其父顶点的左儿子顶点，同时增加一个０权０边长的附加顶点做为右儿子顶点。然后对于其他儿子项点以相同方式作为新增附加顶点的左儿子顶点，一直继续下去，直至处理完所有顶点。所得到的二叉树与树Ｔ具有相同的最小耗费。(A set F that consists of the k vertices o
3-19有向树独立K
- 要求有向树Ｔ的k个独立顶点组成的集合Ｆ，使cost(F)=∑min w(x)*d(x,u)的值达到最小。与有向树k中值问题类似，把有向树变转成为与之等价的二叉树，设Ｔ的以顶点x为根的子树T(x),其左、右儿子顶点分别为y和z。(A set F that consists of a k independent vertex to a tree T is required to minimize the value of the cost (F) = min w (x) *d (x, U). Sim
create_kd_tree
- 通过计算方差，确定划分坐标轴，最后，将点云划分到一个个格子中，这样的好处在于可以将噪点和有效点分别存储于不同的格子中，方便进行去噪。(k-d tree create;By calculating the variance, the coordinate axis is determined. Finally, the point cloud is divided into a number of lattices. The advantage is that the noise and the