FBP

1. ExImage是在DIB类的基础上，扩展了一些基本图像处理功能，除图像格式库外，所有图像处理算法均为自主开发，各处理算法可单独使用。 2. ExImage主要功能包括：颜色增强、校正、配准、拼接、复原、融合、分析、压缩编码等。 关键词：彩色图像处理、自适应中值滤波、图像增强、几何校正、图像配准、图像拼接、图像融合、图像压缩。 (ExImage Image processing toolkit)

词袋模型，非常好的参考，有详细说明，希望顺利使用。(The bag of words model, a very good reference, there are detailed instructions, hope to use smoothly.)

说明：  数字识别，能识别0到9这几个数字啊，基于matlab(Digital identification, can identify it 0-9 a few figures ah, based on matlab)

说明：  高斯混合背景建模MATLAB代码，用于背景提取，获得前景(Gaussian mixture background modeling MATLAB code for background extraction, to obtain the foreground)

已知Radon变换（CT原始数据）利用中心切片法则重建CT图像(Reconstruction using central slice theorem)

可以用于图像的canny滤波处理，直接输入图像，可以保存下来处理后的canny图像(It can be used for canny filtering of images, directly inputting images, and preserving processed canny images.)

从matlab矩阵生成stl格式文件，适用于多孔介质图形生成(Generating STL format files from MATLAB matrix)

对超声图像的去噪和分割，可以显示肿瘤图像的边缘和面积(For ultrasound image denoising and segmentation, image can show the tumor edge and area)

说明：  MATLAB下的人脸定位程序，程序小巧易懂，适合初学者研究进而演化出识别率更好的程序(Under MATLAB Face Locator, the program small and easy to understand, suitable for beginners through research programs evolve better recognition rate)

说明：  UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。(Its influence has reached 70% in 2015. Then it became the baseline that most of the medical image semantic segmentation tasks, and inspired a large number of researchers to think about the U-shaped semantic segmentation network. In the aspect of natural image understanding, more and more SOTA models of semantic segmentation and object detection begin to pay attention to and use U-shaped structure, such as semantic segmentation, discriminative feature network (DFN) (cvpr2018), feature pyramid networks for object detection (FPN) (CVPR 2017), etc.)