New Compressed (zipped) Folder

图像分割算法，包括matlab代码，说明文档和运行结果截图(Image segmentation algorithms, including the matlab code, documentation and operating results screenshot)

包括最后计算压缩图像的峰值信噪比和压缩效果的源码,包含特征值与特征向量的提取、训练样本以及最后的识别，毕设内容，高光谱图像基本处理。( Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, Complete set content, basic hyperspectral image processing.)

对脉搏波的波峰波谷以及重搏波拐点的检测，挺好用的。。(On the pulse wave dicrotic wave peaks and valleys as well as the inflection point detection, very good use. .)

这是一个数字图像处理常用的功能的集成软件，用VC++ 编写，可以对图像进行一些常见的操作，也可以自己扩展功能(This is a common feature of digital image processing integrated software, images can perform basic operations)

基于JPEG的图像篡改检测代码 平台matlab (Platform based on the JPEG image tamper detection code matlab)

说明：  图像评估指标FSIM，传统全参考图像质量衡量标准FSIM（feature similarity），仿效SSIM的计算构成，同样由两部分耦合的幂指数，FSIM由PC项和GM项耦合(FSIM, the traditional full reference image quality measurement standard (FSIM), is composed of two parts of power index, FSIM is composed of PC term and GM term)

结合图像处理MATLAB的基本原理和知识，详细介绍了手指静脉预处理，图像分割，静脉纹路分割，滤波去噪，细化等相关内容。(Combined with the basic principle and knowledge of image processing, this paper introduces the finger vein pretreatment, image segmentation, vein vein segmentation, filtering and denoising, thinning and other related contents in detail.)

图像检测的算法，Faster R-CNN算法，先对整张图像进行卷积计算，然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合，得到候选区域对应的特征矢量，这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一，方便后续的分类工作。通过感兴趣区域池化层处理卷积特征，并将得到的特征送往两个并行计算任务进行训练，分类和定位回归。通过这些方法和改进的框架，Fast R-CNN 用更短的训练和测试时长，取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

1、读取显示所给PET图像（SE2IM210）。 2、学习imhist函数，画出所给PET图像的直方图。 3、根据直方图为该PET图像选择合适的显示窗（imshow参数），使胸 部轮廓等解剖结构可见。 4、根据直方图选择合适阈值，分割肿瘤区域与正常区域，计算肿瘤区 域内图像灰度均值、方差、最大值及最小值。(1 give PET images displayed, read (SE2 IM210). 2, learning, imhist function, draw the give PET image histogram. 3, according to the histogram for the PET image to choose the appropriate display window (imshow parameters), make bosom Outline of anatomical structures such as visible. 4, according to the histogram, choose appropriate threshold segmentation tumor area and normal area, calculating the tumor area In the domain of image grayscale average and variance, the maximum and minimum values.)

计算电磁波辐射的FDTD程序源码，基于C++编写，支持MUR和PML边界条件，能计算SAR及天线辐射方向图(FDTD source code based on C++.Various absorbing boundary, include PEC, Mur, PML Calculate Specific Absorption Rate (SAR) Antenna radiation simulation, calculate radiation power.)