histeq

该程序主要用于图像处理中直方图均衡化，语言环境VC++(The program is mainly used for image processing, histogram equalization, the language environment VC++)

说明：  利用改进的PSO算法对kmeans算子进行聚类，优化了步骤(The improved PSO algorithm is used to cluster kmeans operators, and the steps are optimized)

该函数可接受输入图像和一个滤波函数，可处理所有的滤波细节并输出经滤波和剪切的图像。(Performs frequency domian filterlying.)

包括harris经典算法、harris-Laplace算法，以及susan算法，鲁棒性好(Harris, including the classic algorithm, harris-Laplace algorithm, as well as susan algorithm, better robustness)

说明：  可以实现肤色分割 RGB色彩空间转换为Ycbcr色彩空间并进行非线性变换后判别肤色(Skin color segmentation)

C# SCADA源码修复，完全可用

里面包含kalman，扩展kalman，无迹kalman，粒子滤波，无迹粒子滤波等源码的实现。(upf--The Unscented Particle Filter )

：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)

实验两张图片的拼接，对于图片中的匹配对，用ransac方法消除错配，并估计出仿射矩阵。然后将一张图进行仿射变换，再实现拼接(Experiment two pictures of the stitching, matching pairs for the picture, with ransac way to eliminate mismatches, and to estimate the affine matrix. Then an affine transformation diagram, and then realize splicing)

图像预处理，包括灰度化、图像去噪、灰度变换、阈值分割、边缘检测，形态学处理(image preprocessing)