搜索资源列表
ccl.rar
- Connected Component Labeling,Connected Component Labeling
achieveconnectedcomponentlabeling
- 用VC++实现图像连通区域标记-Image using VC++ to achieve connected component labeling
liantongyufenxiWITHCPP
- 图像连通域标记c语言程序,在VC++环境下可执行,连通域分析是在图像处理中常用的分析方法,希望本代码可以帮助到需要的人。-Image connected component labeling c language program
MPCA
- 高维PCA 参考文献: MPCA Multilinear Principal Component Analysis of Tensor Objects-High-dimensional PCA References: MPCA Multilinear Principal Component Analysis of Tensor Objects
connected-component-labeling
- 背景相減法&連通域標記來實現目標偵測 包含去陰影、形態學運算、框出目標物-Background subtraction & connected component labeling to achieve the target detection
ApplicationsOfDepth-FirstTraversal
- 1. 用DFS判断一个无向图是否是连通图; 2. 为有向图的边分类,将它们的边分为前向边、后向边和交叉边; 3. 用DFS和点消除求有向图的拓扑排序; 4. 判断有向图是不是强连通图,若不是,求强连通分量; 5. 判断有向图是不是半连同图; 6. 判断有向图是不是单连通图; 7. 判断无向图是不是双连通图。 通过以上编程对DFS的应用,进一步了解DFS的算法及它所代表的算法思想。 -1. Using DFS to test if a given undirecte
pca
- PCA主成分分析,用于人脸识别,特征提取等-PCA principal component analysis for face recognition, feature extraction, etc.
Component-extraction
- Component-extraction.rar实现彩色图像的RGB三分量的提取,并将各个分量图像进行均衡化-Component-extraction.rar to achieve color image of RGB three-component extraction, and the various component images equalization
pca
- PCA代码 主成分分析代码 适合初学人脸识别的朋友学习使用-PCA principal component analysis source code suitable for beginner learning to use face recognition friend
matlab-pca
- 这是一个主成分分析的matlab程序,非常有用。-This is a principal component analysis of matlab procedures, very useful.
KPCA
- 为解决PCA不适合多指标综合分析中非线性主成分分析的问题 ,采用核主成分分析 (kpca)方法 ,对我国不同地区 16种腐乳的品质进行了综合评价。 -PCA is not suitable to address the many indicators of a comprehensive analysis of non-linear principal component analysis of the problem, using Kernel Principal Component An
hhh
- :由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效, 提出 应用独立成分分析( I C A) 方法对声音信号进行特征提取, 并证明了这种 I C A 变换能增强语音和音 乐信号的超高斯性. 在此基础上, 应用 I C A基函数作为滤波器, 通过阈值化的去噪方法对含有强高 斯背景噪声的声音信号进行去噪仿真实验. 结果表明, 本方法明显优于传统的均值滤波和小波去噪 方法, 为强背景噪声下弱信号的检测提供 了新的途径.-: As many of the t
Pca
- pca可以对数据进行降维,在模式识别中有重要应用。-pca pricipal component analysis
PCA
- PCA 主成分分析在人脸识别中的应用 基于主成分分析理论对不同人脸库进行学习 总结“经验”并将“经验”用于对人脸的识别中-PCA Principal Component Analysis for Face Recognition Based on principal component analysis theory of different learning face database summary of " experience" and " experience
PCA
- 主成分分析,人脸识别,模式识别,对图像处理有点帮助-Principal component analysis, face recognition, pattern recognition, image processing for a little help
ImageLabel
- java实现图像连通区域算法 *ImageLabel is an algorithm that applies Connected Component Labeling *alogrithm to an input image. Only mono images are catered for.-*ImageLabel is an algorithm that applies Connected Component Labeling *alogrithm to an input
kernel-ica1_1
- 基于核函数的主分量分析法源代码,可用于人脸识别-Kernel-based principal component analysis source code, can be used for face recognition
ComponentbasedFaceDetection
- Abstract We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level,
PCAexample
- principle component analysis example
chengxu
- 本程序应用了取两次阈值、基于特征的逻辑、二值形态学和相连成分的标识,确定了钢的显微图像中颗粒的边界,标识了不同的颗粒。-This procedure applies to take the two threshold values, based on characteristics of logic, binary morphology and connected component labeling, to determine the microstructure of steel grain