Chapter-1

说明：  图像处理所涉及的256转灰度，二值化等等，每个部分都是独立可执行的工程文件，很方面的代码复用(256 involved in image processing turn gray, binary, etc., are independent of each part of the project executable file, it' s code reuse)

说明：  图象压缩方面的论文 可用于毕业设计方面的工作(Image compression can be used for graduate thesis work of design)

Pyramid methods in image processing

主动轮廓模型结合一个特殊的显著性方法实现目标分割(using active contour with a special salient detction modle to segment an image )

图像拼接！！此为本人的毕业设计源码，能完整远行，希望能对大家起到帮助。强烈鄙视那些为了骗取下载数而上载一些无关紧要代码甚至是编译错误一堆的卑鄙小人(Image mosaic! ! This is my graduation project source code can be a complete separation, I hope everyone can help. Strongly despise those who cheat in order to download and upload some irrelevant code compiler error or even a bunch of despicable villains)

填充区域图像，设置填充区域，填出图像的颜色(Filled regional images, set fill area, fill in the color of the image)

：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)

基于alpha matting的抠图技术(Alpha matting based matting techniques)

里面有注释，还有sift和surf的比较(There are notes, there are more sift and surf the)

这是我的图形学的作业，实现光线跟踪。也是学别人做的，仅供学习用。(this is a study work about computer graphics , realizing tracking )