affine-invariant-moment

遥感图像分类工具箱，包含多种基于支持向量机分类器的主动学习算法(active learning toolbox, used for remote sensing data classification, includes most popular svm-based active learning algorithms.)

Tracking based on background subtraction

说明：  指定图像位置和大小的灰度共生矩阵的提取,程序自己编得,蛮好用的(Extraction of gray level co-occurrence matrix of specified image position and size)

最小噪声分离变换（Minimum Noise Fraction Rotation，MNF Rotation）工具用于判定图像数据内在的维数（即波段数），分离数据中的噪声，减少随后处理中的计算需求量(Minimum Noise Fraction Rotation (MNF Rotation) tool is used to determine the intrinsic dimension (i.e. band number) of image data, separate the noise in data, and reduce the computational requirements in subsequent processing.)

奇异值分解降噪程序，压缩包里有程序的详细说明，内附有word文档的说明(SVD noise reduction program, compression bag has a detailed description of the program, enclosing a word document description)

基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理 (Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixels, that is, to converge to the same point as a starting point to the category and then assigned to this type of labeling of these starting points, at the same time to contain too few pixels to remove the category. Then, using thresholding segmentation method of binary image processing)

一个基于模糊阈值的图象分割,是基于MATLAB编程的,希望对大家有所帮助!(a threshold based on the fuzzy image segmentation is based on MATLAB programming, we want to help!)

通过一阶重建方法实现总变差的图像算法源码，希望对学习超分辨率的朋友有所帮助。 (Algorithms and software for total variation image reconstruction via first-order methods)

：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)

实现了车辆目标的背景建模、前景检测、阴影消除、车辆跟踪(Achieve a vehicle target background modeling, foreground detection, shadow elimination, vehicle tracking)