Morph

：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)

对BMP文件进行图像处理，包括：边缘检测、频域变换、几何变换、图像变换校正、灰度变换、图像编码、平滑和匹配(BMP files for image processing, comprising: an edge detection, a frequency domain transformation, geometric transformation, image transformation correction gradation conversion, image coding, and matching the smooth)

基于PCL的估算散乱点云的法矢量，并进行颜色渲染显示(estimation of normal vectors for point clouds and color rendering display based on PCL)

边沿检测与提取，轮廓跟踪的图象算法 用VC++实现，下载可以正常运行(Edge detection and extraction, contour tracing algorithm using image VC++ Realization can download the normal operation of)

打开一个图像或者视频文件，相应的轮廓检测功能，视频运动物体的跟踪功能(applications on image，tracking objects in the video)

（1）在工具栏中点击“打开”功能出现文件打开窗口，用户选择一幅bmp图片后在指定区域居中显示该图片，若图片尺寸小于指定区域则按原始大小显示；若大于则按合适比例显示。 （2）鼠标指针在图片上停留时显示为十字形，同时在控件下方固定位置显示鼠标所在的图像x坐标和y坐标（如：50，100），以图片左上为坐标原点。 （3）在工具栏中可以点击控件实现在图片上绘制一次直线、矩形或圆等图形，在绘图过程中要求图片和图形一直可见，不能出现刷新痕迹，绘制的图形可以任意修改形状。 （4）可以任意选择图形线条的类型（虚线或直线）、颜色（三种）及粗细（三种），要求选择方式可以叠加，绘制过程要求可任意连续执行。 （5）在工具栏中点击“保存”功能出现文件保存窗口，用户可以选择保存路径来保存当前图片操作结果（包括图片和图形）。 （6）点击“关闭”功能，关闭当前图片，若操作结果尚未保存则必须弹出对话框提示用户进行保存。 ((1) in the tool bar, click on Open function has the file open window, the user selects a bmp image to center the picture in the designated area, if the image size is smaller than the specified area press the original size if more than press the appropriate scale display. (2) is shown as a cross when the mouse pointer over the image, while displaying an image x and y coordinates of the mouse cursor in a fixed position below the control (eg: 50,100) to image the upper left of the coordinate origin. (3) in the toolbar you can click controls to achieve a draw straight lines, rectangles or circles and other graphics on the picture, the requirements of pictures and graphics in the drawing process has been seen, it can not appear refreshed traces drawn graphics can be modified shape. (4) can choose the type of graph line (dashed or straight), color (three kinds) and thickness (three kinds), asked to choose the way can be superimposed, can arbitrarily draw process requires continuous exe)

视觉测量中编码点与非编码点的匹配的方法实现(Vision measuring, coding point and the coding point matching )

mean shift功能演示，实现图像分割，具体请看效果(the demonstration of the mean shift algorithm)

本代码实现的功能：24位位图（BMP）的灰度化。具体实现过程：打开图像文件,找到数据区(每3个字节代表一个象素的R,G,B值),循环扫描,根据RGB的权重,计算出灰度值. 如果要得到256色灰度图,则要新建一个调色板,修改信息头和文件头.(Realize the function of the code: 24-bit bitmap (BMP) of the gray. Concrete realization of the process: Open the image file, locate the data area (3 bytes per pixel on behalf of a R, G, B values), cyclic scan, according to the weight of RGB, gray value is calculated. If you want to get 256 colors grayscale, would create a new palette, modify the header and file header information.)

平常经常用到的一个小程序，两个矩阵相乘的求法(Common often USES a small program, two of the matrix multiplication is also given)