image-segmention

图片浏览源码例程，程序调用API函数实现图片的浏览。包括浏览文件夹对话框的“SHBrowseForFolderA”，以及配合它使用的从列表ID取路径“SHGetPathFromIDListA”(Picture Viewer source code routines , the program calls API functions for image browsing. )

主分量分析对SAR图像目标进行特征提取，用最近临方法进行分类(Principal component analysis（PCA） of SAR image target feature extraction, classification using nearest-neighbor method)

数字图象处理，模式识别，人脸识别，包括了程序和一个人脸库，是相当好的学习东西(Digital image processing, pattern recognition, face recognition, including the procedures and a face database is very good to learn things)

c#下实现图像颜色k均值聚类 将像素点颜色分类并合并(c# implementation of color image k-means clustering to classification of color pixels and merge)

在图像的DCT系数上嵌入信息，图像为常见的512*512大小的灰度图（实验所用图像为LENA.BMP）。嵌入的信息为一二进制Logo图（实验所用logo图为tj-logo.bmp），该图大小为32*32，共1024个Bit。 1. 在LENA图中嵌入水印，输出嵌入后的水印图像。 2. 对水印图像解码，输出解码后得到的logo图，并统计错误率。 3. 使用不同的嵌入强度进行嵌入，观察其对作品直觉效果和解码效果的影响，绘制相应的曲线。 4. 使用加入噪声、改变对比度、JPEG压缩等攻击方法对水印作品进行攻击，查看解码的效果。 (Information embedded in the DCT coefficients of the image, the image of the common 512* 512 size gray-scale (experimental image LENA.BMP). Information embedded in a binary Logo Figure (used in the experiment logo picture shows the tj-logo.bmp), the plot size 32* 32, 1024 Bit. LENA Figure embedded watermark, the watermark image output embedded. (2) logo watermark image decoding, the output decoded Figure statistical error rate. (3) using a different embedding strength for embedding, observe the works intuitive effects and decoding results, and draw the corresponding curve. (4) by adding the noise, change the contrast, JPEG compression attack methods to attack, to see the effect of decoding the watermark works.)

实现椭圆几何畸变矫正，将原始椭圆形变的图像，矫正为矩形正常图像(The correction of elliptical geometric distortion is achieved, and the original elliptical image is corrected to rectangle normal image.)

能够很好的实现车牌定位的matlab源程序.但是不能实现车牌字符分割(Able to realize a very good source matlab plate positioning. But not license plate character segmentation)

根据Dalal提出的HOG特征算法编写，代码简单，注释清楚，根据所处理图像的名称路径修改后可生成36*105的特征向量。(The algorithm proposed by Dalal HOG feature writing code simple, clear annotation feature vector can generate 36* 105, modified according to the name of the image processing path.)

对两幅图像进行配准，分别提取两幅图像的surf特征点以及描述子，得到粗匹配结果，然后根据粗匹配结果，采用ransac方法计算基础矩阵，并去除误匹配点，得到较准确匹配结果(Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descriptor, then according to the results, the coarse matching results, using RANSAC method to calculate the fundamental matrix, and eliminate the error matching, gain a more accurate matching results)

8邻域边缘连接，实现闭合边界跟踪，简单实用(8 neighborhood edge connector, to achieve closure boundary tracking, simple and practical)