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矩形检测：用霍夫直线检测方法来对图像中的矩形进行检测。开发环境：vc6，需要安装OpenCV。OpenCV只是用来读取和显示图像等简单操作。(Rectangle detection: Hough line detection method using the image of the rectangle to test. Development Environment: vc6, need to install OpenCV. OpenCV is used to read and display images such as simple operation.)

实现原始的JPEG图片编解码，不借助任何的第三方库，纯C++。(To achieve the original JPEG image decoding, without using any third-party libraries, pure C++.)

说明：  本代码采用的是opencv，有视频采集和对采集的视频帧进行laplace处理的功能！(This code uses a opencv, with video capture and the capture of video frames laplace processing capabilities!)

改程序是用来行人检测的，有源代码，可以轻松学习并参考。(The reform program is used for pedestrian detection, source code, you can easily learn and reference.)

图像处理中的数字识别代码，包括数字训练集合和训练分类算法，可以实际应用。(digital recognition)

运动目标的给总与识别，可以实时显示目标的运动轨迹，支持高速相机实时处理视频数据，并可以获得运动目标的轮廓矩等信息(Moving target to the total and the identification of moving targets in real-time display track, real-time processing to support high-speed video camera and moving target access to information such as contour moments)

基于visual c++平台，相机标定方法中的直接线性变换方法，即DLT方法(Based on visual c++ platform, camera calibration method of direct linear transformation method, namely DLT method)

用OpenCV进行图像处理与工程开发的一个入门示例程序——Hough变换，效果不错(using OpenCV for image processing and development of a project portal sample program-- Hough Transform good results!)

根据RBM深度网络（Hinton，2006）进行MINST手写文字的分类器的训练，利用训练得到的权值制作了这个小程序，通过这个程序可以看出训练结果对数据集内的测试样本和训练样本都能进行很好的识别，但是对其他的手写字体识别就没有那么好了。(According to RBM depth network (Hinton, 2006) conducted MINST handwritten text classifier training, using the training to get the right value to produce this small program, this program can be seen through the results of the training data set and the training sample test samples are can be well identified, but on the other handwriting recognition is not so good.)

：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)