Tensor-Voting

利用卷积神经网络进行特征提取，并将卷积特征可视化(Feature extraction using convolutional neural networks and visualization of convolution features)

极坐标图像转直角坐标图像，相当于球面的展开效果，最好是正方形图像，但是长方形也可以。(POLARTOIM converts polar image to rectangular image)

可对灰度及彩色图像进行压缩及解压缩，通过变量名可以实现基于不同方式（多级树集合分裂spiht，ezw,stw,wdr等）的压缩，形如wcompress( c ,X,SAV_FILENAME,COMP_METHOD)(The wcompress command performs either compression or uncompression of grayscale or truecolor images.)

关于图像融合的工具箱，从国外网站上找到的，很好用，可以作为对比实验时使用。包含一些需要处理的源图像。(On image fusion toolbox, from abroad to find the site, very good, and can be used as a comparative experiment. Contains a number of source images need to be addressed.)

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)

一篇关于光声成像的图像重建算法研究，开发工具为matlab，罗列了很多理论知识(A photoacoustic imaging of image reconstruction algorithms, development tools matlab, listed a lot of theoretical knowledge )

指纹识别算法，开源，十分有用，是matlab语言编写的，直接可以用再做修改(about matching，very useful )

雷达信号回波仿真 多普勒(Echo Doppler radar signal simulation)

里面包括了MATLAB的好多程序,主要有灰度切分，直接灰度变换，直方图规定化，直方图均衡化等(Which includes many of the MATLAB program, there are gray-scale segmentation, direct gray-scale transformation, Histogram of the provisions of, such as histogram equalization)

利用核函数进行遥感图像的分类，本程序再支持向量机的基础上引入了核函数进行图像分类，是分类效果更加明显(Use of nuclear function remote sensing image classification, the program no longer support vector machine based on the introduction of the kernel function for image classification, classification effect is more pronounced)