DCT+DWTmatlab数字水印代码

二维Renyi熵图像分割源代码（原创） 参考文献见源代码。(source code for two-dimensional Renyi entropy thresholding algorithm)

说明：  一、课题介绍 本设计为基于MATLAB的火焰烟雾火灾检测系统。传统的采用颜色的方法，误识别大，局限性强。结合火焰是实时动态跳跃的，采用面积增长率，角点和圆形度三个维度相结合的方式判断是否有火焰。该设计测试对象为视频，通过下一帧和上一帧的差异发现是否有火情，并可发出语音报警。本设计带有一个人机交互式GUI界面，界面友好。是个不错的bishe选题。 二、算法流程 结合火焰的面积增长率，角点和圆形度三个维度综合判断。并且得出每帧图像火焰部分的该三个参数，实时显示在GUI上。 三、GUI界面设计(This design is based on MATLAB flame smoke fire detection system. The traditional method of using color has large error recognition and strong limitation. Considering that the flame is real-time and dynamic jumping, the area growth rate, corner and roundness are combined to determine whether there is flame. The test object of the design is video. Through the difference between the next frame and the previous frame, whether there is fire is found and the voice alarm can be sent out. This design has a personal computer interactive GUI interface, the interface is friendly. It's a good choice for BiShe.)

分别使用灰度值变换和频域滤波的方法实现图像的伪彩色处理。在Matlab中编程实现(The use of gray value, respectively, transform and frequency domain filtering method of pseudo-color image processing. Programming in Matlab in)

采用基于信道非均匀性校正改进的RLS算法实现对探测器的非均匀性校正(An improved RLS algorithm based on channel nonuniformity correction is used to correct the nonuniformity of detector.)

说明：  DIP关于各噪声和滤波概述，里面包含PDF和Latex(Dip overview of noise and filtering)

人脸检测的方法很多，kl是其中比较有效的一种。本代码主要是使用pca的方法进行人脸检测，可以检测出人脸的大小位置，数目，大小(Face Detection many means kl is a more effective. The code is the use of the method pca Face Detection can detect the size face location, number, size)

极化SAR图像处理中 相干目标的 KROGAGER分解程序(Polarization SAR image processing in coherent goal decomposition process KROGAGER)

说明：  包含多个函数，可直接调用（需按注释输入参数），对ppm和pgm文件，读、写和多种图像处理。(Ppm and pgm file for reading and writing, and various image processing)

基于多分辨率的NSST变换工具箱，用于图像融合效果好，且速度优于NSCT。(Based on Multi-resolution NSST conversion kit for image fusion effect is good, and the speed is better than NSCT.)

用于实现高密度人群聚合检测 包含：边缘检测、灰度共生矩阵计算、SVM特征分量的分类(For achieving high-density population polymerization detector comprises: edge detection, gray level co-occurrence matrix calculation, classification SVM characteristic component)