region_growing

28个vc++图像处理原程序之--对比度拉伸(28 vc++ Image processing of the original procedure- contrast stretching)

利用直方图双峰法选取适当阈值对图像进行二值化处理。此法只适用于直方图为双峰状图像。(Using the Shuangfeng method to the appropriate threshold histogram of image processing two values. This method is only applicable to the histogram of image of Shuangfeng.)

双边滤波器，对彩色图像进行处理，效果非常好！(bialteral filter)

各向异性扩散相干斑降噪，可以在增强边缘的同时滤出噪声，效果很好！(Anisotropic diffusion, coherent noise can enhance the edge of the filter out the noise at the same time, the effect is very good!)

可对灰度及彩色图像进行压缩及解压缩，通过变量名可以实现基于不同方式（多级树集合分裂spiht，ezw,stw,wdr等）的压缩，形如wcompress( c ,X,SAV_FILENAME,COMP_METHOD)(The wcompress command performs either compression or uncompression of grayscale or truecolor images.)

Opencv处理图像，用ostu法实现图像的二值化(Opencv image processing, using ostu method to achieve image binarization)

：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)

使用K近邻算法对一个2维德样本集进行分类，样本集的分布为混合高斯分布。(K nearest neighbor to use a 2 Verde sample set for classification, the distribution of sample set for mixed Gaussian distribution.)

Z. Liang, J. Xu, D. Zhang, Z. Cao, L. Zhang, "A Hybrid L1-L0 Layer Decomposition Model for Tone Mapping," in CVPR 2018.

国外的牛人写的软件，数据降维工具箱，包含了几十种常用的流形学习方法的源码，自带图形界面。(A foreign master write this dimension reduction toolbox,there s dozens of code of common manifold learning methods. )