simon haykin 的自适应滤波原理教程，关于自适应滤波器设计一般背景的优秀教材...

呵呵，本人目前没有编程序，以后会传的。现在先给大家分享一些资料-Ha ha, I is not programmed, it will spread. Now give some information to share with you

模拟足球场绘制，可逼真绘出3D造型，可旋转，改变视角等-Simulated soccer field map can be drawn realistic 3D modeling, can be rotated to change the perspective, etc.

用Matlab实现的Canny边缘检测算法，压缩文件中附有一张实验图片-Using Matlab realize the Canny edge detection algorithm, compressed file attached to an experimental image

梯形低通滤波,最后显示处理结果. 直接解压后将图片和程序放在同一个文件夹里.-trapezoidal low-pass filter, the final results showed. Photo will be directly extracted and procedures on the same document folder.

基于轮廓的形状特征提取 1)该程序在VC＋＋6.0下编译通过； 2)测试图片说明：（图像路径名请在main函数中fp=fopen("1.bmp","rb")中设定） 64×64 24bit BMP： 图像1、2用来验证旋转图像识别；图像1、3用来验证平移图像识别；图像1、4用来验证缩放图像识别； 120×120 24bit BMP： 图像5、6用来验证平移图像识别；图像7、8用来验证旋转图像识别； 3)结果说明： MCS.dat保存了Mountain Climbing Sequence。 -Based on the outline of the shape feature extraction 1) the program in VC 6.0 under the compiler through 2) Test Photo Caption: (image path name in the main function fp = fopen (1.bmp, rb) set) 64 × 64 24bit BMP: image 1,2 to verify rotating image recognition image 1,3 to verify translational image recognition image 1,4 to verify scaling image recognition 120 × 120 24bit BMP: images used to verify translational 5,6 image recognition images 7,8 rotate images used to verify identification 3) The results indicate that: MCS.dat preserved Mountain Climbing Sequence.

simon haykin 的自适应滤波原理教程，关于自适应滤波器设计一般背景的优秀教材-adaptive filter priciple by Simon Haykin

一个描述计算几何的相当不错的程序，写的挺好，对于初学者大有好处-a geometric description of the calculation procedure is quite good, very well written, is good for beginners

本源程序提供了一种基于串口的LED显示方式.该方法操作方便,二次开发也很方便.-This source provides a serial port based on the LED display. This method is easy to operate, the second development is also very convenient.

Watershed segment the contents is about watershed

利用opencv实现的快速模板匹配算法 Fast Template Matching Algorithm，提供Demo图像-use opencv achieve rapid template matching algorithm Fast Template Match ing Algorithm for Image Demo