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利用Mean-shift进行平滑处理，主要是对图像进行平滑的界面，MFC和OpenCV进行混合编程……(Mean-shift smoothing, image smooth interface, the MFC and OpenCV mixed programming ...)

k均值聚类的动态分类算法 灰度图和彩图的 (k-means clustering algorithm for the dynamic classification of grayscale and color)

基于自动种子选择的区域生长法，克服了传统区域法的很多缺点(Region growing method based on automatic seed selection, to overcome the many shortcomings of the traditional regional law)

深度图像的快速实现增强的算法，效果非常好，但是速度很快(Quick to realize the depth of image enhancement algorithms, the effect is very good, but fast)

说明：  一种ENVI编程语言，用于ENVI二次开发便于处理高光谱图像(An envi programming language, used in the secondary development of envi, easy to process hyperspectral images)

：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)

该程序可以将一附图片中的人的脸部轮廓检测出来，以作后续处理。(The program can be attached to a film of people' s facial contours detected for follow-up.)

用SIFT结合光流场进行图像配准，得到不错的效果(The image registration is carried out with SIFT combined with optical flow field, and good results are obtained.)

说明：   最简单的差分格式有向前、向后和中心3种。 向前差分：f (n)=f(n+1)-f(n) 向后差分：f (n)=f(n)-f(n-1) 中心差分：f (n)=[f(n+1)-f(n-1)]/2(The easiest difference format forward, backward, and three kinds of centers. Forward differencing: f (n) = f (n+ 1)-f (n) Backward difference: f (n) = f (n)-f (n-1) Central difference: f (n) = [f (n+ 1)-f (n-1)]/2)

非局部平均，用来去除灰度图里面的噪声，效果明显(Probability Non-Local Means)