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这次上传的代码是关于MRI Brain Scan_segmentation用的代码，希望能对大家有用(this code is uploaded on Scan_segmentation Brain MRI of the code, we hope that the useful)

3d 图形学的 算法，包含源代码(3d graphics algorithms, including source code)

说明：  摄影测量相对定向程序，单独像对相对定向，所得精度较高(Photogrammetry relative orientation procedures, as a separate relative orientation, derived from high precision)

运用四边形网格实现医学图像皮肤组织的三维重建算法，对于医学图像的三维重建非常有用(Using quad to reconstruct the contour of skin , it is very useful for medical image reconstruction)

说明：  图象处理的融合算法集合,有8种融合算法,这是我硕士课题中的源代码.以函数的形式给出,肯定能用(The integration of image processing algorithm for the collection, there are eight kinds of fusion algorithm, this is my master s degree in the subject s source code. In the form of function is given, certainly can)

Cox 论文、程序matlab代码，仿真结果以及读书笔记，有助于学习(Cox papers, procedures matlab code, simulation results, as well as study notes, contribute to learning)

基于采集的脑电导联信号，用matlab去噪和伪迹，最后绘制脑地形图。 去噪文件夹： FastICA_25文件夹为 fastICA函数；pca为pca 函数； av_sub伪迹减法去噪； ica_step1，ica_step2&pca 为ica与pca的去噪程序； wave_entro为小波熵的计算程序； topoplot为地形图函数； topo为画地形图程序；eloc32,eloc16为函数调用32导/16导的头皮电极分布位置 get_txt将采集的脑电数据转化为文本格式； readdata为依次读取文件夹中的一系列txt文件。(Based on the collection of brain conductivity signal, denoising using matlab, and pseudo-tracks, the final topographic map of brain mapping. Denoising folder: FastICA_25 folder function for FastICA pca function for PCA av_sub pseudo-trace subtraction de-noising ica_step1, ica_step2 )

将保存有灰度图像信息的txt文件转化生成灰度图片。 txt数据只能包含数据，数据用逗号或者空格隔开，所存数据个数应与所生成的图片大小相同。例：要还原的图片大小为640*480，那么txt文件中就应当有307200个数据。(The txt file with information of grayscale image is transformed into gray image. The TXT data can only contain data, separated by commas or spaces, and the number of data stored should be the same as the size of the generated image. Example: the size of the image to restore is 640*480, so there should be 307200 data in the txt file.)

该程序包含多个水下目标检测的增强算法（暗通道、引导滤波、MSR等）(The program includes several enhancement algorithms for underwater target detection (dark channel, guided filtering, MSR, etc.).)

说明：  SRM空间富模型隐写分析算法,选区高维特征，使用集成分类器进行训练(Spatial Rich Model Steganalysis Algorithms, Selection of High-Dimensional Features, Using Integrated Classifier for Training)