pinghua

Unix系统下，小波包变换的实现源码实例，，当然，稍作改动也可用于windows系统下。 (Unix systems, the instance of the wavelet packet transform source, of course, some minor changes can also be used in the windows system.)

DOG检测算子，能够构造高斯尺度空间，并检测其中的极值，从而得到有价值的特征点位置(DOG detection operator, the Gaussian scale space can be constructed, and tested one of the extremes, resulting in valuable feature position)

一种新的彩色图像去噪与增强模型(A new color image denoising and enhancement model)

超分辨率重构，介绍超分辨率重构最大后验概率算法。(Super-resolution reconstruction, introduced the super-resolution reconstruction of maximum a posteriori probability algorithm.)

这个源代码是用来实现图形之间的基本运算的，包括求交集、并集、差集等等。而且图形的形状也可以任意选择，可以是圆、线段、点、多边形等等。(the source code is used to achieve the basic graphics operations, including for intersection and set difference, and so on. The shape and graphics can choose freely, it can be won, segment, points, polygons, and so on.)

说明：  VC的一些工程案例，包括车牌识别，人脸识别，图像处理基本操作等(car license recognization system)

运动模糊图像 图像复原 维纳滤波复原 逆滤波复原 VC++6.0程序 代码(Motion-blurred image restoration image restoration Wiener filtering inverse filtering to recover VC++ 6.0 code)

基于分块的人脸图像超分辨率学习算法实验平台，利用学习到的训练集图像信息对低分辨率图像超分辨率，并可以查看超分辨率后的图像块匹配结果（用鼠标双击结果显示窗口中的实验图像），可以设置实验参数。(Block-based face image super-resolution algorithm for the experimental platform to learn, learning to use the training set of low-resolution image information super-resolution images and can view the image super-resolution block-matching results (double-click with the mouse results display window of the experimental images), the experimental parameters can be set.)

利用opencv库写的一个关于图像孔洞填补的小程序，附加了效果图片(Using the opencv library to write a small program on the filling of the hole, the additional effect of the picture)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)