CSC.EGBIS.JSEG.Meanshift

kinect扫描到的人体分离出来，并以二值图显示，且可以保存在本地，丰富的注释帮助理解(kinect body scan to be separated, and binary image display and can be saved locally, rich annotation to help understand)

说明：  基于LBP纹理特征的运动目标检测算法.rar(Moving target detection algorithm based on LBP texture features. Rar)

基于菲涅耳全息图和离散余弦变换的_盲数字水印_技术(Blind digital watermarking _ technology based on the Fresnel hologram and the discrete cosine transform _)

对各种格式图像实现打开保存旋转的操作的示例工程的说明文档。(images of various formats to achieve preservation of open rotation examples of the operation of the project documentation.)

对输入视频进行处理，使用高斯背景建模的方法，得到视频前景，对于人群密度估计具有很大作用。(Processing of the input video, using a Gaussian background modeling method, to get videos prospects for population density estimation has a significant role.)

运用于人脸图像预处理中，主要实现功能有：人脸检测，人脸对齐，人脸图像放缩，人脸剪切。(Applied to the face image preprocessing, the main features are: face detection, face alignment, face image zoom, face cuts.)

VisualC++数字图像处理技术详解第2版光盘-第八章 分水岭分割算法(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter VIII watershed segmentation)

VapourSynth-BM3D-master BM3D算法的原创代码，运行速度超快，参考文献《Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering》(VapourSynth-BM3D-master BM3D original algorithm code, run super-fast, Reference " Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering" )

：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)

一段很好的图像放大缩小程序，解码后可以运行。(A very good image magnification narrow program, the decoding can run. )