xiaobotuxiangchuli

本代码是做指纹识别是代码，平台是MATLAB，能够完成指纹识别、处理、匹配整个过程。(This code is to do fingerprint identification code, the platform is MATLAB, able to complete the fingerprint identification, processing, matching the whole process.)

多分类问题的支持向量机源程序一对一方法 绝对可以运行(Multi-class SVM using One-Against-One decompositionoao)

bmp图像边缘检测，vc界面，很好用，适合初学者学习(bmp image edge detection, vc interface, useful for beginners to learn)

Gabor小波提取特征,然后用支持向量机作分类器，可以用于掌纹，人脸，指纹识别。(Gabor wavelet feature extraction, and then use the support vector machine as classifier, can be used for palmprint, face, fingerprint identification.)

基于最优条件下三次B样条小波的多尺度边缘检测算子(Based on the optimal conditions of cubic B spline wavelet multi-scale edge detection operator)

均值滤波和中值滤波 图像是一种重要的信息源，通过图像处理可以帮助人们了解信息的内涵,然而在图像使用和传输过程中,不可避免会受到噪声的干扰，因此为了恢复原始图像，达到好的视觉效果，需要对图像进行滤波操作。根据噪声种类不同，可以采用不同的滤波方法，均值滤波是典型的线性滤波算法，能够有效滤波图像中的加性噪声，而中值滤波器是能够有效滤除脉冲噪声的非线性滤波器，但传统中值滤波去脉冲噪声的性能受滤波窗口尺寸的影响较大, 在抑制图像噪声和保护细节两方面存在矛盾。本文首先对不同均值滤波器在处理不同噪声方面的优缺点进行了分析，然后分别用中值滤波器和自适应中值滤波器对被椒盐噪声污染的图像进行了滤波操作，发现自适应中值滤波方法不仅可以有效滤波椒盐噪声，同时还可以有效地克服中值滤波器造成图像边缘模糊的缺点。(Mean filtering and median filtering)

基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)

一种基于纹理特征分析的遥感图像分类C++程序(Based on the analysis of texture characteristics of remote sensing image classification C++ program)

条形码生成工具：利用此工具可自动生成条形码(Bar code generation tool)

主要功能是实现相机标定，以及实现反投误差，实现特征点的提前。(The main function is to achieve camera calibration and error to achieve the anti-investment, to achieve the feature points in advance.)