WirelessSensorNetSimulationP

实现信号的离散傅立叶变换和反变换以及滤波，平滑处理(Realize signal discrete Fourier transform and inverse transform, as well as filtering, smoothing treatment)

This program draws an image from a bmp file

数字图像处理同态滤波器 数字图像处理 冈萨雷斯二版 p153例4.10(Homomorphic filter Digital Image Processing Digital Image Processing Gonzalez p153 cases 4.10)

说明：  kl变换. KL变换的流程如下： （1)获取多幅图像的统计特性：图像之间的协方差 （2)获取协方差的特征值和特征向量，从而获取正交核:特征矩阵 （3)图像对应象素点对各个特征向量进行投影运算。(kl transformation)

中值滤波，取出奇数个数据进行排序，用排序后的中值取代要处理的数据即可(median filtering.rar)

说明：  Pixel design for H-noise cal; imageJ platform ; test done

给出了偏微分方程的径向基函数配点法的数值解法，进行了数值模拟，并且给出了图形(The numerical solution of the radial basis function with partial differential equation method, numerical simulation, and gives the graphic )

这是基于VC++编写的对遥感影像分析的程序，主要是对遥感影像进行PCA变换，使得对影响分析不仅可以依托于现成软件，我们自己也可以开发(This is written in VC++ based on remote sensing image analysis program, mainly for remote sensing images PCA transform, makes impact analysis can not only rely on ready-made software, we can also develop their own)

asift算法进行图像配准，具有较好的结果，调试完成，可用(image registration, asift algorithm has a better result, debugging is complete, the available)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)