Image Compression(v.2)

Harris角点检测，关于角点的应用在图像处理上比较广泛，如图像匹配(FPM特征点匹配)、相机标定等。网上也有很多博客对Harris角点检测原理进行描述，但基本上只是描述了算法流程，而其中相关细节并未作出解释，这里我想对有些地方做出补充说明，正所谓知其然知其所以然，如有不对，还望指正。(Harris corner detection)

用matlab实现基于形态学的边缘检测和孔洞填充(Using matlab to achieve morphological edge detection and hole filling)

基于小波变换的图像融合源程序，通过调试，能直接使用。(Image fusion based on wavelet transform source code through the debugger can be used directly.)

将获取图像分块成10*10，并合成列向量的矩阵(Will acquire an image block into 10* 10, and matrix synthesis column vector)

快速TV算法用于图像去模糊、图像去噪等，其中TV模式包括两种(TV went fast algorithm for image blur, image denoising, which includes two TV mode)

说明：  Jsteg_Mu可批量对JPEG图像进行Jsteg隐写。 Jpeg_Or 为jpeg密写的原始图像的文件夹， Jpeg_Di 为jpeg密写后密写了的图像的文件夹,code_file 为密写的文件,rate 为密写嵌入率.(Jsteg_Mu JPEG images can be batch Jsteg on steganography. Jpeg_Or jpeg steganography for the original image folder, Jpeg_Di as jpeg steganography written after the close of the images folder, code_file written document for the dense, rate for the steganography embedding rate.)

在知道清晰图像和噪声频率特性的情况下，维纳滤波器被看做是一种有效的图像复原技术(In that resolution images and noise frequency characteristics of the case, the Wiener filter is seen as an effective image restoration technique)

对于图像不同纹理特征值（能量、熵、惯性矩、相关）的提取(For different image texture feature value (energy, entropy, moment of inertia, related) extraction)

说明：  压缩传感理论的一个简单例子，首先对信号进行稀疏采样，然后利用MP算法对信号进行重建。(Compressed sensing theory of a simple example, first of all, the signal sparse sampling, and then use MP algorithm of signal reconstruction.)

针对杂波环境下的多个机动目标跟踪问题, 本文将多模型概率假设密度 (Multiple-model probability hypothesis density, MM-PHD) 滤波器和平滑算法相结合, 提出了 MM-PHD 前向 – 后向平滑器. 为了避免引入复杂的随机有限集 (Random finite set, RFS) 理论, 本文根据 PHD 的物理空间 (Physical space) 描述法推导得到了 MM-PHD 平滑器的后向更 新公式. 由于 MM-PHD 前向–后向平滑器的递推公式中包含有多个积分(By integrating the multiple-model probability hypothesis density (MM-PHD) filter with the smoothing al- gorithms, an MM-PHD forward-backward smoother is proposed in this paper for tracking multiple maneuvering targets in clutter. To avoid use of complex random finite set (RFS) theory, the backward updated equation of the MM-PHD smoother can be derived according to the physical-space explanation of the PHD)