grabcut_matlab

各个坐标系之间的坐标变化：地心大地坐标到地心空间大地直角系，地心系和测站系，测站系和测站系等之间的变化，旋转，平移。(Coordinate changes between the various coordinate systems: geocentric geodetic coordinate system to the geocentric Cartesian space earth, geocentric system and station lines, change the station, such as lines and lines between stations, rotation, translation)

关于图像处理的一些基本操作，空间、频域图像增强，形态学处理，边缘检测等(About some of the basic image processing operations, space, frequency domain image enhancement, morphological processing, edge detection, etc.)

基于不精确拉格郞日算子法的低秩矩阵重构程序，可以用于图像分割，将目标图像分割为背景和前景，从而将前景分离出来。(Lagrangian method based on imprecise low rank matrix reconstruction procedures can be used for image segmentation, the target image into background and foreground, which will separate the foreground.)

说明：  利用k-means算法进行图像分割，效果较好(Using k-means algorithm for image segmentation is better)

包括计算共生矩阵、对共生矩阵归一化、对共生矩阵计算能量、熵、惯性矩、相关4个纹理参数、求能量、熵、惯性矩、相关的均值和标准差作为最终8维纹理特征 (Including the calculation of co-occurrence matrix, normalized to the co-occurrence matrix, co-occurrence matrix calculation on the energy, entropy, moment of inertia, related to four texture parameters, find the energy, entropy, moment of inertia, related to the mean and standard deviation as the final eight-dimensional texture feature)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)

最为经典的ROA和ROEWA算子，用于SAR图像边缘检测，并附有一定的实例图像，以说明实验结果。(ROA and ROEWA algorithms are given in order to detect the edge of images, and some images are attached illustrating the experimental results.)

图像处理作业：基于高频补偿算法实现图像滤波及分析 数值分析作业：四种方法实现求解根号3的值(Image processing operations: image filtering and analysis of numerical analysis operations: four methods based on high-frequency compensation algorithm to solve the root of the value)

说明：  Donoho提出的小波阀值去噪的基本思想是将信号通过小波变换（采用Mallat算法）后，信号产生的小波系数含有信号的重要信息，将信号经小波分解后小波系数较大，噪声的小波系数较小，并且噪声的小波系数要小于信号的小波系数，通过选取一个合适的阀值，大于阀值的小波系数被认为是有信号产生的，应予以保留，小于阀值的则认为是噪声产生的，置为零从而达到去噪的目的。尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点。(The basic idea of wavelet threshold de-noising proposed by Donoho is that the wavelet coefficients generated by the signal after passing through the wavelet transform (Mallat algorithm) contain the important information of the signal. After the wavelet decomposition, the wavelet coefficients of the signal are larger, the wavelet coefficients of the noise are smaller, and the wavelet coefficients of the noise are smaller than the wavelet coefficients of the signal. By selecting a suitable threshold, the wavelet coefficients of the noise are larger than the valve The wavelet coefficients of values are considered to be generated by signals, which should be preserved. The ones less than the threshold are considered to be generated by noise, which is set to zero to achieve the purpose of denoising.)

使用VC++编程，自动检测角点，实现图像配准，代码可用。(Automatically detect the corner points, for image registration, the code is available.)