ReadDICOM.tar

通过准确的图像预处理和最小外接矩形方法，准确定位斑点位置和求取面积(Accurate location and area of spots are located accurately by accurate image preprocessing and minimum external rectangle method)

说明：  提取图像中的模式噪声，是先使用滤波器后再进行提取的(To extract the pattern noise in an image, a filter is used first and then the noise is extracted)

说明：  使用MATLAB求图像的形状特征-7个不变矩，亲自测试过可以用(Using MATLAB to find the shape feature of the image - 7 invariant moments, which have been tested and used personally.)

基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)

KLDIV Kullback-Leibler or Jensen-Shannon divergence between two distributions.

gif格式图片生成，包括动画生成，是非常好的入门学习资料。(gif format image generation, including animation generation, is a very good learning materials.)

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)

说明：   奇怪的分式 上小学的时候，小明经常自己发明新算法。一次，老师出的题目是： 1/4 乘以 8/5 小明居然把分子拼接在一起，分母拼接在一起，答案是：18/45 （参见图1.png） 老师刚想批评他，转念一想，这个答案凑巧也对啊，真是见鬼！ 对于分子、分母都是 1~9 中的一位数的情况，还有哪些算式可以这样计算呢？ 显然，交换分子分母后，例如：4/1 乘以 5/8 是满足要求的，这算做不同的算式。 但对于分子分母相同的情况，2/2 乘以 3/3 这样的类型太多了，不在计数之列!,四个四个数不同 请写出所有不同算式的个数（包括题中举例的）。 思路：循环遍历。 dfs c++(dfs c++)

基于matlab的三维重建代码，提取两幅图片的匹配点(Based on three-dimensional reconstruction of the matlab code, extract the two images match point)

说明：  可用于简单图像的处理及几何特征提取，可得到1. 长宽比 2. 矩形度 3. 圆形度 4. 面积凹凸比 5. 周长凹凸比 6. 球形度 7. 偏心率 8. 形状参数(It can be used to process simple images and extract geometric features. It can obtain 1. aspect ratio 2. rectangularity 3. roundness 4. area concave-convex ratio 5. circumference concave-convex ratio 6. sphericity 7. eccentricity 8. shape parameters.)