Convlution3D-master

该算法实现车牌快速自动识别,建立了车牌字符库和车牌图象,可以直接运行(The algorithm quickly realize automatic license plate recognition, license plate character set up libraries and license plate images can be directly run)

彩色图像的arnold图像置乱和logistic混沌序列的图像加密程序matlab源码(Color image of arnold image scrambling and logistic chaotic sequence of image encryption program Matlab source)

该代码实现了基于graph cut的图象分割算法,具体代码是用c++编写的,但提供了matlab接口,分割效果较好.(The code based on the graph cut image segmentation algorithm, the specific code is used c++ Prepared, but provides a matlab interface, better segmentation.)

说明：  有注释的粒子滤波程序。粒子滤波(PF: Particle Filter)的思想基于蒙特卡洛方法(Monte Carlo methods)，它是利用粒子集来表示概率，可以用在任何形式的状态空间模型上。(Annotated particle filter program. Particle filter (PF: Particle Filter) Monte Carlo method based on the idea (Monte Carlo methods), which is set to represent the probability of a particle can be used in any form of state space model.)

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.)

ICA独立分量图像特征提取,内含源程序和图片，程序完整、易懂，有很好的参考价值。(The ICA independent component image feature extraction，Contains the source program and pictures, complete, and easy to understand and have a very good reference value. )

TurboPixels超像素生成算法，matlab运行，(TurboPixels super pixel generation algorithm)

使用sift算法，找出图像的关键点，使用关键点作为匹配因子对两幅图像进行匹配，数据库中包含了丰富的可用图片库。(Use sift algorithm to find the image of the key points, key points to use as matching factor of two images match, the database contains a wealth of available gallery.)

图像处理标准测试图片库，用于数字图像处理各种仿真实验。(Standard test image gallery image processing to digital image processing all kinds of simulation experiments.)

说明：  图像分割，还可以，kmeans聚类的方法。k-means算法是一种聚类算法，所谓聚类，即根据相似性原则，将具有较高相似度的数据对象划分至同一类簇，将具有较高相异度的数据对象划分至不同类簇。(K-means algorithm is a clustering algorithm. The so-called clustering is to divide data objects with high similarity into the same class cluster and data objects with high dissimilarity into different class clusters according to the similarity principle.)