资源列表
tuxiangxiufu
- 基于稀疏分解的图像信息修复，图片展示了一被“破坏”lina图像，采用此程序可以得到较好修复-Sparse decomposition based on image information restoration, image shows a being " destroyed" lina image, the use of this procedure can get a better fix
rgb2luv
- 根据彩色图像中RGB转LUV原理，自己编写了算法，实现了RGB到LUV颜色空间转换，亲自调试过代码，可以用(According to the color image RGB to LUV principle, I wrote the algorithm to achieve the RGB to LUV color space conversion, personally debugging the code, you can use)
Kalman-Filter-for-Beginners-master
- 实现简单的卡尔曼滤波，包括线性卡尔曼，扩展卡尔曼，无迹卡尔曼等(Realization of simple Calman filter)
shiyongmatlab
- matlab的一些基本实用算法，包括插值与拟合，规划问题，数据分析，解方程等-matlab some basic practical algorithms, including interpolation and fitting, planning, data analysis, solving equations, etc.
AIalgorithm
- 人工智能算法文档：包括专家系统，模糊匹配算法，搜索算法等-including Searching Algorithm，Practical Fast Searching in Strings
DormitoryManageSystem
- 对宿舍学生管理表要求完成如下功能： （1）：录入宿舍学生信息 （2）：添加宿舍学生信息 （3）：删除宿舍学生信息 （4）：修改宿舍学生信息 （5）：显示宿舍全部学生信息 -Students of the dormitory management table required to complete the following functions: (1): Input dormitory Student Information (2): Add a dormito
TrueGrid
- TureGrid是很好的有限元建模软件，软件很小，但画出的网格质量非常高-TureGrid is a good finite element modeling software, the software is small, but very high quality paint grid
Classicalalgorithm
- 数学建模比赛常用的算法,包括插值与拟合、线性规划、整数规划、动态规划、遗传算法等-Competition commonly used mathematical modeling algorithms, including interpolation and fitting, linear programming, integer programming, dynamic programming, genetic algorithm
Pattern_recognition_with_neural_networks_in_C.pdf.
- 本资料为神经网络模式识别及其应用的中文版，其中，介绍了模式识别方面的基础知识，以及神经网络的应用，并且给出了C++代码的实现。非常适合入门。-This information and the application of neural network pattern recognition of the Chinese version, which introduced the basic knowledge of pattern recognition and neural network a
yuyingbianma
- 里面有五个文件，是我精心收询的语音编码的程序，有用c写的，也有用matlab仿真的
fet
- 用遗传算法写的软件,非常有用! 值得大家一看!-using genetic algorithms to write the software, very useful! Worth a look!
1
- SIFT特征匹配算法的PPT，很适用,是学习SIFT的好资料.-SIFT feature matching algorithm of the PPT, it is applicable, is a good learning SIFT information.