搜索资源列表
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
SDL.Component.Suite.v6.0.Pro.For.C5D56.with.Source
- SDL控件组也是不错的,可以做化学分子结构图;信号FFT分析;画科学工程图等等。 和ABAKUS一样,其源码难以得到,极其困难!现在你可以拥有源码了-SDL is also a good control group, they can do chemical molecular structure diagram FFT signal analysis drawing of science engineering drawings and so on. And ABAKUS as i
Aligrid
- StringGrid component with aditional options
Fault1_PCA
- 利用主成分分析方法,对TE模型产生的故障数据故障1进行故障检测-Using principal component analysis, on the TE model failure data generated by a fault detection fault
Advanced.Export.Component.v3.47
- 多种模式导出工具,Delphi源码,支持多种表格 ,是一个很好的工具-Export tool for a variety of models, Delphi source code, supports a wide range of forms, is a good tool
CPort4
- Best Delphi 2010 Component for Serial Port
xxd
- 二值图像连通域标记快速算法,能进行区域检测,附代说明文档-Binary image connected component labeling fast algorithm that can detect regional, with a generation of documentation
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
MSIC10_8_4
- Product: MiTeC System Information Component Suite with source code Version: 10.8.4
eWebEditor_ASP_V7.3_CHS_GB2312
- 编辑器介绍: eWebEditor是一个基于浏览器的在线HTML编辑器,WEB开发人员可以用她把传统的多行文本输入框<textarea>替换为可视化的富文本输入框。 eWebEditor主功能不需要在客户端安装任何的组件或控件,操作人员就可以以直觉、易用的界面创建和发布网页内容。 您可以通过eWebEditor自带的可视配置工具,对eWebEditor进行完全的配置。 eWebEditor是非常容易与您现有的系统集成,简单到您只需要一行代码就可以完成
ICA
- 独立成分分析算法降低原始数据噪声,并提取特征值,非常有用得数据去噪程序。-Independent component analysis algorithm reduces the raw data noise, and extract characteristic value, is more helpful data denoising procedure.
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
NonlinearICA_Toolbox
- 非线性独立分量分析(ICA)源码,主要是用于非线性ICA进行盲源分离算法的函数-independent component analysis (ICA)
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
DlpZip
- 一个delphi中压缩的组件VCLZip,有帮助文件-Delphi in a compression component VCLZip, there are help files
OReilly - COM and .NET Component Services Source C
- OReilly - COM and .NET Component Services Source Code-OReilly- COM and. NET Component Services Source Code
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.
ClusteringToolbox2
- 一个用于聚类的工具箱,内有主元分析、模糊等技术的Matlab源代码和应用实例程序Demo。-One for clustering toolbox, with principal component analysis, fuzzy techniques, such as Matlab source code and application procedures for Demo.
ICA_with_Reference
- We present the technique of the ICA with Reference (ICA-R) to extract an interesting subset of independent sources from their linear mixtures when some a priori information of the sources are available in the form of rough templates (references). T