targettrackingusingkalman

盲去卷积复原算法，并在MATLAB中进行实验，和维纳滤波的复原效果进行一个对比。盲去卷积的方法有多种，露西-理查德森算法属于图像复原中的非线性算法，与维纳滤波这种较为直接的算法不同，该算法使用非线性迭代技术，在计算量、性能方面都有了一定提升(The blind deconvolution restoration algorithm is carried out in MATLAB and is compared with the restoration effect of Wiener filtering. There are many ways of blind deconvolution. Lucy Richardson algorithm is a nonlinear algorithm in image restoration. Unlike Wiener filtering, this algorithm uses nonlinear iteration technology, and has a certain improvement in computation and performance.)

说明：  该文件里含有多个图像增强算法，包含图像去雾，对比度增强，引导滤波，限制对比度自适应直方图均衡，直方图均衡等多种算法。(The file contains several image enhancement algorithms, including image defogging, contrast enhancement, guided filtering, contrast limiting, adaptive histogram equalization, histogram equalization and so on.)

对于空间两个点云，利用经典的icp算法进行匹配(ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. )

说明：  图象处理的融合算法集合,有8种融合算法,这是我硕士课题中的源代码.以函数的形式给出,肯定能用(The integration of image processing algorithm for the collection, there are eight kinds of fusion algorithm, this is my master s degree in the subject s source code. In the form of function is given, certainly can)

说明：  有关连通域的提取，以肺部图片作为示例，使用bwlabel函数，非常方便(Connected domain on the extraction of the lung images as an example, the use of bwlabel function, very convenient)

说明：  一系列处理图片算法，简单的图片放大，打开时请打开project902.dsw，直接编译请重新下载(A series of image processing algorithms, simple image enlargement)

用于去除眼动，眼漂的插件。下载后解压到eeglab 插件目录下，启动matlab 设置好路径就可以自动打开，在eeglab tool 中可以选中使用。(A plug-in for removal of eye movement and eye drift. After downloading, unpack it to the eeglab plug-in directory, start MATLAB and set up the path to open automatically, which can be optionally used in eeglab tool.)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)

说明：  机器学习专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技 能，为包括人脸识别领域的人工智能的发展做出了极大的贡献。本文探索如何应用机器学习中的一些技术，使计算机更好地完成人脸识别领域中的人脸检测和人脸验证。 在人脸检测方面，针对如何快速、准确地检出人脸的问题，基于DLIB中特征模型，可以快速提取检测出人脸，并且提取出人脸特征点,主要作用就是快速检测定位人脸。在训练过程中，引入了ResNet机器学习算法，该算法采用多层卷积神经网络结构，对人脸进行多层特征提取和描述，得到人脸特征描述符。通过测试可以证实了上述方法能够检测和识别人脸。(In terms of face detection, for the problem of how to detect faces quickly and accurately, based on the feature model in DLIB, you can quickly extract and detect faces, and extract face feature points, the main role is to quickly detect and locate faces. In the training process, the VGG-FACE machine learning algorithm is introduced. This algorithm uses a multi-layer convolutional neural network structure to perform multi-layer feature extraction and description on the face to obtain the face feature descriptor. Tests can confirm that the above method can detect and recognize human faces.)

摄像机标定程序，实现单目，双目摄像机的标定(calibration of camera )