BPFA_Inpainting

八种图像置乱方法：希尔伯特变换、采样置乱、ZIGZIG置乱、等等等等(Eight kinds of image scrambling method)

实现了在Winform中图片的拖拽移动及缩放，用到了PictureBox控件.

此MATLASB源代码是LSB的图像数字水印的嵌入与检测，希望对需要者有用!(This source code is MATLASB image watermarking LSB embedding and detection, in the hope that those who need it useful!)

目前MATLAB里边对于多点计算仿射矩阵普遍采用RANSAC法，但是比较麻烦计算量也相对较大，通过最小二乘法来进行仿射矩阵的估计能够大大提高该效率，所以奉上自己在实验室写的仿射矩阵估计函数，来弥补matlab函数库在这方面的不足。(The MATLAB inside for calculating affine matrix commonly used RANSAC method, but more trouble calculation is relatively large, estimated by the least squares method to the affine matrix can greatly improve the efficiency, so give yourself in laboratory to write the estimated affine matrix function, to compensate for the lack of MATLAB Library in this area.)

视觉slam十四讲，程序资料：g2o优化、cere拟合、特征点提取与匹配、点云拼接、opencv图像读取(Visual slam 14, program data: g2o optimization, cere fitting, feature point extraction and matching, point cloud mosaic, OpenCV image reading)

实现数字图像水印DWT域的水印可以进行水印的嵌入与提取(Realization of digital image watermark embedding and extracting watermark in DWT domain for watermarking)

hit-miss方法下的图像骨架提取，很好用(hit-miss method of image skeleton extraction, very good use)

运动检测方法的实现， 这是最原始的方法实现，根据不同的应用可以对其进行相应的改进(Motion detection method realize that this is the most primitive method, according to different applications can be improved accordingly)

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)

：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)