Digitalwatermark

画矩形的代码，包含各种算法，具体的自己看吧，这是图形处理的作业之一(Draw a rectangle of the code, contains a variety of algorithms, specifically for yourself, this is one of the graphics processing jobs)

实现了图像的灰度化并进一步用于视频监视控，一些自适应信号处理的算法，计算加权加速度。( Achieve a grayscale image and further control for video surveillance, Some adaptive signal processing algorithms, Weighted acceleration.)

通过sift算法进行图像配准，配准效果好，精度高(Image registration algorithms sift through the good registration results, high precision)

radarsat1 的成像程序，包括源码，是个很好用那个的算法(radarsat1 imaging procedures, including source code, is a good use of that algorithm)

索书号文字图像分割，粘连字符切分，彩色车牌分割 ，文字识别的结构特征提取子函数(Call text image segmentation, character segmentation, color plate segmentation, text recognition feature extraction Functions)

实现基于混合高斯模型的运动目标检测算法的仿真(Achieved based on Gaussian mixture model simulation of moving target detection algorithm)

最近编写了一些极对数坐标转换的程序，这是最初编写的那个，希望对你们有用(Here is a log-polar image transform code.)

计算机图形学 c语言实现的直线段裁剪算法(Computer graphics c language to achieve a straight line segment clipping algorithm)

该算法先通过七点进行M估计，然后挑选三点进行精确匹配运算(This algorithm firstly the fuzzy matching to achieve slightly at the beginning of localization, to achieve accurate positioning on the basis of improve the accuracy simulation)

局部二进制模式，LBP，是已被用于纹理特征的一个 分类。在本文中，提出了一种基于使用这些功能的方法 检测缺陷图案的面料。在培训阶段，在第一个步骤LBP算子是 施加到无缺陷织物样品，逐个象素和参考的所有的行（列） 特征矢量的计算。那么这个图像被分为Windows和LBP算子是 应用这些窗口的每一行（列）。根据与参考比较 特征向量一个合适的阈值，无缺陷的窗户被发现。在检测阶段中，一个 测试图像被划分成的窗户，并使用阈值时，有缺陷的窗口可以 检测到。该方法简单，灰度不变。由于其简单性， 线上实现是可能的。(Local Binary Patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well. )