hs_opticalflow

实现bmp位图的图像放大缩小，包含整个mfc的源码文件。(Achieve bmp bitmap image zoom, contains the entire mfc source files.)

灰度图像的属性计算：利用bwlabel和regionprops函数对度量二值图像中目标对象的属性。(Attribute gray image: Using bwlabel and regionprops function measure binary image of the target object attributes.)

实现图像文件（如bmp等）的读取、显示、真彩图转换成灰度图、灰度图转化为二值图及绘制图像直方图功能。(Achieve an image file (such as bmp, etc.) to read, display, true color conversion to grayscale, converted to grayscale and binary image histogram function to draw the image.)

说明：  基于自适应α-β滤波器的接收机跟踪环多普勒自补偿算法(Based on adaptive alpha beta filter satellite receiver loop of the doppler compensation algorithm design)

分水岭算法的实现，能自动对图像进行分割，任何图像都能够读取(Watershed algorithm, can automatically split the image)

林健写的Matlab在图像处理与目标识别方面的应用实验，包括染色体识别与统计、汽车牌照定位与字符识别、基于BP神经网络识别字符的简单实验。 (Lam Kin Writing at the Matlab image processing and target recognition application experiments, including chromosome identification and statistics, vehicle license positioning and character recognition, based on BP neural network easy to identify characters in the experiment.)

说明：  交互式支持向量机图像分割程序，通过鼠标选取区域实现前景和背景选取(Interactive support vector machine image segmentation procedures, through the mouse to select regions of foreground and background selection)

基于模糊贴近度的SAR图像变化检测，包括算法流程及具体代码(Based on the fuzzy nearness of the SAR image change detection, including the algorithm process and the specific code)

说明：  由正则表达式构造DFA的程序，并可以对输入串模拟正则表达式匹配(This is a C program used in compiling the given regular expression into an equivalent DFA, simulated through tracking State sets.)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)