与计算机图形学有关，DDA划线法，可以划直线，调用可划多边形...

经典书籍-数字图像处理-冈萨雷斯--2。解决各位敲代码的烦恼！These MATLAB functions were created for the book Digital Image Processing Using MATLAB (DIPUM), by R.C. Gonzalez, R.E. Woods, and S.L. Eddins, Prentice Ha-Classic books- digital image processing- Gonzalez 2. To resolve the trouble code you knock! These MATLAB functions were created for the book Digital ImageProcessing Using MATLAB (DIPUM), by RC Gonzalez, RE Woods, andS.L. Eddins, Prentice Ha

遗传算法用于菌落图像分割。遗传算法其固有的并行性和不易陷入局部最优的特点使之非常适于大规模搜索空间的寻优，使图像分割产生的误差最小，为解决图像分割中的参数选择难题提供了有力的保障。附有程序清单-A New Model for Image Segmentation

这是我前一段时间收集的一个关于图像处理的代码，真可谓是丰富多彩。一定会您有用。里面涉及各种关于图像处理的源代码。-this is a magic image processing sourc code, whic i got the other day. it must be useful for people who research image compression

一个比较好的图像处理源程序，可以对图像进行多种样式处理。-a relatively good source image processing, image processing for a variety of styles.

这个也是图形学的一个作业，不过功能比较全面和强大，可以完成基本几何体的造型、多边形化，可以设置场景光源并进行光线跟踪渲染等。启动类为final_3d.Final_3D-this is a graphics operation, but function more comprehensive and powerful, can be completed basic geometric shapes, polygons, and can be installed Scene light source and Ray Tracing rendering, etc.. Start category final_3d.Final_3D

canny算子 铝箔 用于图像边缘检测 和高斯铝箔-canny operator foil for image edge detection and Gaussian foil

计算机视觉一书（马颂德）中的标定程序，使用了书中提供的数据，简单供大家参考-First Book of Computer Vision (Ma Songde) in the calibration procedures, the use of book data provided by a simple reference for everyone

边沿检测与提取，轮廓跟踪的图象算法 对着两种算法有着详细解释-Edge detection and extraction, the image contour tracking algorithm into a detailed explanation of the two algorithms

这是一个关于三维图像数据距离误差程序的源代码，希望对大家有用。-This is a three-dimensional image of the data from the error"s source code, we hope that the right useful.

（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 -(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.