Image-gradation-processing

说明：  有一个matlab 神经网络学习的例子，解压缩后可以直接运行(There is a matlab neural network learning examples can be run directly after decompression)

This book is an introduction to two subjects:Matlaband numerical computing. This first chapter introducesMatlabby presenting several programs that inves- tigate elementary, but interesting, mathematical problems. If you already have some experience programming in another language, we hope that you can see how Matlabworks by simply studying these programs.

机动目标“当前”统计模型及其自适应算法的仿真程序，包含与Singer模型的对比，周宏仁一书第八章上有理论内容。(kalman filter, Singer Model, self-adjusting algorithm)

《可编程控制器实验教程》配书程序4XXXXXXXXXXXXXXXXXXXXXXXXXX(" Programmable Controller experimental course" assigned program 4XXXXXXXXXXXXXXXXXXXXXXXXXX)

computer vision toolkit tutorial

彩色噪声图像的curvelet基下的增强，高斯白噪声，显示不仅去噪且增强效果好(Color noise images curvelet based enhancement under the Gaussian white noise, showing not only a good denoising and enhancement)

Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy.

小波分析的经典案例，很适合初学者学习。。。。。(Wavelet analysis of the classic case, very suitable for beginners to learn)

说明：  用matlab编写的fft快速傅立叶变换及逆变换函数,其中flip1是供ditfft函数调用的倒叙子函数。(using Matlab fft prepared by the fast Fourier transform and inverse transform function, which is flip1 ditfft function call for the Warrior unreachable.)

说明：  AMplitude mod and demodulation