灰度处理

该源码为目标检测和跟踪算法 包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (The source code for target detection and tracking algorithm include: 1 Static background background prediction target detection 2 Static background frame difference method for target detection 3. Mean Shift tracking method 4 multi-target tracking methods focus)

稀疏自适应匹配追踪算法，无需稀疏度，就可以重构原始信号。(Sparse adaptive matching pursuit algorithm, without sparsity, we can reconstruct the original signal.)

进行纯净信号和加噪信号的时频域显示，及其包络图显示，并计算加噪信号的信噪比、互相关系数、峭度值，对信号进行离散余弦变换。(The time-frequency domain display of pure signal and noise-added signal and its envelope diagram display are carried out, and the signal-to-noise ratio, correlation coefficient and kurtosis value of noise-added signal are calculated.Making Discrete CoSine transform for signal)

visual c++ 工具 c语言 实现的灰度共生矩阵。(visual c++ tool c language GLCM.)

KLT point match:Detection and Tracking of Point Features.

：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)

MATLAB数字图像处理，图像加噪声处理(MATLAB digital image processing, image plus noise deal)

目标运动的真实轨迹和估计轨迹，以及所进行的误差分析(Target motion trajectory and estimate the true trajectory, and error analysis conducted)

k均值图像分割，经典的图像分割方法，算法简单，效果好。(k-means image segmentation)

遥感数字图像处理基础 遥感数字图像处理基础(yao gan shu zi tu xiang chu li ji chu)