wode1

这是一个图像处理时用到的程序 可作为图像处理用非常有价值(This is an image processing procedures can be used as image processing with very valuable)

图像处理，可以用来进行图像内插。效果还不错的！(image interpolation)

城轨车辆轮对踏面故障在线监测系统，基于振动检测法的轮对踏面故障监测系统，通过傅里叶变换、小波包分析对釆集到的振动信号进行分析获得信号的特征,研究了基于短时能量判断的踏面故障特征提取算法。(On line monitoring system for wheel tread of urban rail vehicles, based on the vibration detection method of wheel tread fault monitoring system, through the Fu Liye transform, wavelet packet analysis of the collected vibration signals to obtain the characteristics of the signal, the study of the short-term energy judgment based on the feature extraction algorithm.)

这个程序实现了光流法对运动物体的识别，效果还行(This program implements the optical flow method to identify moving objects, the results were OK)

平稳移不变小波变换，及图像去噪应用，PSNR, SSIM图像质量评估(stationary wavelet for image denoising)

画图源程序-图像数据显示，可以对数据进行时间轴的曲线显示，也可以是空间轴的多维显示 (Paint source- the image data, the data can be curve shows the time axis, it can be multi-dimensional display space axis)

CV水平集模型实现对图像的分割，收敛快，收敛效果优于GAC 模型。(CV levelset model to realize image segementation)

多种图像增强方法，效果都挺好的的。包括dct，灰度变换，直方图均匀化，图像锐化(variety of image enhancement methods, the results are very good. Including Extra, the gray-scale transformation, histogram equalization, image sharpening, etc.)

典型的一维kalman滤波原理程序，容易掌握！(typical one-dimensional Kalman filtering process easy!)

图像分割是把图像划分成具有实际意义的互补交迭的区域的集合。在图像分割之前，图像区域的数目未知，而在分割后各个区域同时满足均匀性和连通性的条件，故图像分割是一个复杂的过程，目前大多数研究都是针对某一类型图像或者某一具体应用的分割。图像阈值化分割的基本思想是确定一个阈值，然后把每个像素点的像素值和阈值相比较，根据比较的结果把像素划分为两类，前景(1)或背景(0)。该方法的关键是确定一个最优的阈值。常用的阈值确定方法有直接门限法、类间最大方差法(otsu法)、分水岭算法、最小误差法、最大熵法等。该段代码就是图像的阈值分割。(The image segmentation is divided into the practical significance of the emerging area of complementary set. In the image segmentation, before the number of image region, the unknown, and after each area in division at the same time satisfy the uniformity and connectivity condition, so the image segmentation is a complicated process now, most research are for a certain type image or a particular application of the division. Image threshold segmentation of the basic idea is to determine a threshold, then put each pixel threshold value of the pixels and compared, according to the result of the comparison the pixel divided into two categories, the prospect (1) or background (0). This method is the key to determine an optimal threshold. Commonly used method to determine the threshold value of direct threshold method, the largest of the variance between method (otsu method), watershed algorithm, the minimum error method, the maximum entropy method, etc. When the code is image threshold segm)