OpenSourceVidProcessing

对于原始数据进行插值并抽取，从这个过程中将数据平滑化，得到理想的效果。(The original data for interpolation and extraction, data from this process will be smoothed to get the desired results.)

基于MATLAB-GUI图形界面的数字图像处理软件 本系统设计基于GUI图形界面，用matlab语言编写代码，实现功能包括图象的读取、存储、显示、直方图均衡化、阈值化、小波分解、小波重构、加噪、去噪、平滑、锐化、边缘检测、图像分割等(MATLAB-GUI graphical user interface based on digital image processing software of the system design is based on GUI graphical interface, using matlab language to write code to achieve functionality, including image reading, storage, display, histogram equalization, thresholding, wavelet decomposition, wavelet reconstruction, add noise, denoising, smoothing, sharpening, edge detection, image segmentation, etc.)

二维地震信号的emd实现，逐道分解，挑选信号主导分量(EMD realization of two-dimensional seismic signal, decomposition by channel, selection of signal dominant component)

指纹识别的MATLAB程序，包含了特征点抽取，特征点匹配，识别。(Fingerprint of the MATLAB program, including the feature point extraction, feature point matching, identification)

说明：  平台基于visual studio2015，使用opencv函数实现中值滤波，进行图像处理(median blur based on opencv)

SIFT算法，可以有效的找到不同画面中的对应特征点，在跟踪和图形识别方面有很好的用途(SIFT algorithm, can effectively find a different screen the corresponding feature points, in the tracking and pattern recognition as a very good use of)

说明：  MATLAB工具箱，《数字图像处理》冈萨雷斯版专用，网上不少人在找，这是我从国外网站下载的，贡献出来。(MATLAB toolbox, )

说明：  功用：利用snake算法实现数字图像的边缘检测，图像分割以及特征提取(function : use of snake algorithm for digital image edge detection, image segmentation and feature extraction)

详细介绍PCNN算法用于lena图像分割的实例，并用matlab实现(Detailed the PCNN algorithm for instance lena image segmentation and implemented using matlab)

基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理 (Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixels, that is, to converge to the same point as a starting point to the category and then assigned to this type of labeling of these starting points, at the same time to contain too few pixels to remove the category. Then, using thresholding segmentation method of binary image processing)