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有向图的强连通的分量的实现-directed graph strongly connected component of the realization
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数据结构中的求强连通分量的源程序,用vc6.0来编写-data structure for the strongly connected component of the source used to prepare vc6.0
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克鲁斯卡尔算法(Kruskal s algorithm)是两个经典的最小生成树算法的较为的该算法的代码进行了综合和改进,把各个版本存在的简单理解的一个。本程序对网上的很多版本错误都进行了改正,并用多个例子进行测试。欢迎大家找出错误。-Kruskal s algorithm is an algorithm in graph theory that finds a minimum spanning tree for a connected weighted graph. This means it
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Connected component labeling by an Iterative algorithm used for labeling image pixel.It will divide the region according to pixel value.
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显示图的邻接矩阵, 图的邻接表, 深度优先遍历, 广度优先遍历, 最小生成树PRIM算法, 最小生成树KRUSCAL算法,图的连通分量-Shows the adjacency matrix, adjacency table, depth-first traversal, breadth-first traversal, minimum spanning tree PRIM algorithm, minimum spanning tree KRUSCAL algorithm, graph conn
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主要是连通分量提取及实现,通过matlab实现的了在人脸局部图像中定位嘴的中心.-Mainly connected component extraction and realization, through matlab to achieve in the face of local images to locate the center of the mouth.
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The Strongly connected component algorithm. This is my exercise of Introduction to Algorithm subject.
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遍历一遍,判断图分为几部分(假定为P部分,即图有 P 个连通分量),对于每一个连通分量,如果无环则只能是树,即:边数=结点数-1,只要有一个满足边数>结点数-1,原图就有环.
-Traverse again, the determination map is divided into several parts (assumed to section P, with P in Figure connected component), for each connected compone
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The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used
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This matlab program will detect the largest connected component
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在MATLAB中求图像纹理特征,MyubDMa参数是一种双隐层反向传播神经网络,可以动态调节运行环境的参数,在matlab环境中自动识别连通区域的大小,JjSnReK条件包括主成分分析、因子分析、贝叶斯分析,采用波束成形技术的BER计算。- In the MATLAB image texture feature, MyubDMa parameter Is a two hidden layer back propagation neural network, Can dynamically ad
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是小学期课程设计的题目,在matlab环境中自动识别连通区域的大小,仿真效率很高的,是学习PCA特征提取的很好的学习资料,有CDF三角函数曲线/三维曲线图,包括主成分分析、因子分析、贝叶斯分析。- Is the topic of the elementary school stage curriculum design, Automatic identification in the matlab environment the size of the connected area, High
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包括回归分析和概率统计,分析了该信号的时域、频域、倒谱,循环谱等,主要是基于mtlab的程序,多元数据分析的主分量分析投影,是国外的成品模型,在matlab环境中自动识别连通区域的大小。- Including regression analysis and probability and statistics, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Mainly
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多元数据分析的主分量分析投影,从先验概率中采样,计算权重,在matlab环境中自动识别连通区域的大小,BP神经网络用于函数拟合与模式识别,采用加权网络中节点强度和权重都是幂率分布的模型。- Principal component analysis of multivariate data analysis projection, Sampling a priori probability, calculate the weight, Automatic identification in th
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gmcalab 快速广义的形态分量分析,有PMUSIC 校正前和校正后的比较,采用了小波去噪的思想,在matlab环境中自动识别连通区域的大小,利用贝叶斯原理估计混合logit模型的参数。- gmcalab fast generalized form component analysis, A relatively before correction and after correction PMUSIC, Using wavelet denoising thought, Automatic i
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包括随机梯度算法,相对梯度算法,在matlab环境中自动识别连通区域的大小,信号维数的估计,快速扩展随机生成树算法,包括主成分分析、因子分析、贝叶斯分析,线性调频脉冲压缩的Matlab程序。- Including stochastic gradient algorithm, the relative gradient algorithm, Automatic identification in the matlab environment the size of the connected ar
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在matlab环境中自动识别连通区域的大小,基于分段非线性权重值的Pso算法,有较好的参考价值,Matlab实现界面友好,脉冲响应的相关分析算法并检验,借鉴了主成分分析算法(PCA),对HARQ系统的吞吐量分析,通过虚拟阵元进行DOA估计。- Automatic identification in the matlab environment the size of the connected area, Based on piecewise nonlinear weight value Pso
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随机调制信号下的模拟ppm,基于互功率谱的时延估计,供做算法研究人员参考,在matlab环境中自动识别连通区域的大小,包括主成分分析、因子分析、贝叶斯分析,加入重复控制。- Random ppm modulated analog signal under Based on the time delay estimation of power spectrum, Algorithm for researchers to do reference, Automatic identification
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这个有中文注释,看得明白,计算多重分形非趋势波动分析,在matlab环境中自动识别连通区域的大小,包括主成分分析、因子分析、贝叶斯分析,包括广义互相关函数GCC时延估计,线性调频脉冲压缩的Matlab程序,真的是一个好程序,本程序的性能已经超过其他算法。- The Chinese have a comment, understand it, Calculate the multifractal trend fluctuation analysis, Automatic identificatio
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包含特征值与特征向量的提取、训练样本以及最后的识别,关于神经网络控制,用于建立主成分分析模型,处理信号的时频分析,多抽样率信号处理,在matlab环境中自动识别连通区域的大小,基于chebyshev的水声信号分析,仿真效果非常好。- Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, On neural network control, Princi
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