TOOLBOX_calib

基于DPCA算法的动目标检测，非常实用，希望对你有帮助(DPCA-based moving target detection algorithm is very useful and would like to help you)

非负矩阵分解法，这一种新型的子空间分解方法，增加了非负性约束，比PCA、ICA更有效(Non-negative matrix factorization method, which a new type of sub-space decomposition method, an increase of non-negative constraint, compared with PCA, ICA is more efficient)

基于灰度共生矩阵的图像检索技术。完整的特征提取、特征描述、特征匹配以及结果返回。(GLCM-based image retrieval techniques. A complete feature extraction, feature description, feature matching and the result returned.)

关于信号分析，小波包分解的程序以及应用方法(The program about signal analysis, wavelet packet decomposition and its application method)

把一副画转化为由彩色字符（8个字符）组成的画。最简单、实用、绿色的软件！！！　　　　提供VB源代码！！！ (The grounds of color into a painting of characters (eight characters), composed of paintings. The most simple, practical, green software! ! ! VB source code provided! ! !)

说明：  实现图像精确分割，基于ALpha Matting 的基础上的Sharedmatting图像分割算法(Accurate image segmentation,Sharedmatting image segmentation algorithm based on ALpha Matting)

说明：  图像边缘检测及图像区域分割、目标检测、目标识别(Image edge detection, image region segmentation, target detection and target recognition)

先用PCA降维，在利用支持向量机进行分类，这个分类是二分类，所以PCA的降维降到两维即可分类。(Firstly, PCA dimensionality reduction is used to conduct classification with support vector machine. This classification is binary classification, so the dimensionality reduction of PCA can be reduced to two dimensions for classification.)

主成分分析的三种方法，一种是自编程序，一种是princomp函数,一种是pcacov函数(Three principal component analysis, one is self-programming, one is princomp function, a function is pcacov)

说明：  首先对车牌识别系统的现状和已有的技术进行深入的研究，然后开发出一个基于 Python 的车牌识别系统，文中先对车辆图像进行高斯去噪、灰度化和边缘检测等预处理方法，然后用颜色特征和形态特征相结合的方法来确定车牌位置，用彩色分割法来完成车牌分割，最后，运用 SVM 分类训练器完成字符识别并使用Python 软件环境进行车牌识别的仿真实验。(License plate recognition based on SVM)