GammaTest_1.1.tar

说明：  在一幅图像中对单人脸进行定位,原理是基于多模板匹配(locate face in one picture in which there is a single face，its theory base on many templates matching)

图象的模糊边缘检测，用MATLAB实现的，有用户界面和检测结果！(Image fuzzy edge detection, implemented using MATLAB, user interface and test results!)

迭代法阈值分割，迭代法自动计算最优阈值。已经写成函数形式，输入灰度图，输出分割好后的阈值图。(Threshold segmentation iterative method, iterative method for automatically calculating the optimal threshold. Function has been written in the form of input grayscale output partition threshold after a good map.)

偏微分方程图像复原代码，其中有频域迭代盲解卷积H1模型，TV去噪模型，自己编的自适应去噪模型，以及改进的自适应去噪模型，令附卫星图片，PSNR代码(Image restoration code of partial differential equations, including iterative frequency-domain blind deconvolution H1 model, TV denoising model, made themselves adaptive denoising model, and improved adaptive denoising model, so attached to satellite images, PSNR code)

matlab数字图像分割，可以提取一幅图中想要的目标，并计算出目标的面积，周长和长宽比。(Digital Image Segmenting Useing Matlab.This program can extract the interesting target from a figure, and calculate the target area, perimeter and aspect ratio.)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )

基于互信息的分层金字塔的图像配准算法，互信息的加入让信息更丰富，精度更高(Image registration algorithm based on hierarchical pyramid of mutual information, mutual information is added to make information more abundant and higher precision)

利用多用方法进行数字图像去模糊实例，便于初学者理解反卷积去模糊、伪逆去模糊等基本算法(Example scripts for image debluring, try to give general introduction of some basic algorithms, such as pseudo inverse debluring.)

说明：  灰度图象的直方图均衡化；梯度；直方图规定化；(Keywords histogram equalization of gray image; gradient; histogram specification;)

稀疏贝叶斯学习的代码，出自UCLA，大家可以下载看看(sparse bayesian learning)