segment

说明：  在VC中实现了使用Moravec算子对遥感影像进行特征点的提取，缺点是速度较慢。不过提取效果不错！(Implemented in VC using Moravec operator of remote sensing images feature point extraction, the disadvantage is slow. However, extracting good results!)

利用位图图像中的冗余数据位，将小文件嵌入到大的图像文件中，并保持原图像不变。(The use of bitmap images in the redundant data bit will be embedded in small files to large image files, and to maintain the original image unchanged.)

基于区域的主动轮廓模型算法，是论文《Active Contours Without Edges》的VC算法实现(Region-based active contour model algorithm, for the paper " Active Contours Without Edges" of the VC algorithm)

说明：  数字图像处理的常用算法总结，比如，直方图，直方图均衡化，均值滤波，中值滤波，平滑滤波等。(Commonly used digital image processing algorithms summarized, for example, histogram, histogram equalization, mean filtering, median filtering, filtering and so on.)

说明：  线形骨架的综述类文献，着重强调定义属性和应用(Curve-skeleton properties, applications, and algorithms)

gdal读tiff图像,获取图像大小，波段信息，图像数据，写Tiff图像到固定路径。(Read and write an TIff image using GDAL)

生成四副条纹图，进行四步相移，并且进行相位解包裹，并以图片形式输出结果(Generate four fringes, were four-step phase shift and phase unwrapping, and output as a picture)

说明：   最简单的差分格式有向前、向后和中心3种。 向前差分：f (n)=f(n+1)-f(n) 向后差分：f (n)=f(n)-f(n-1) 中心差分：f (n)=[f(n+1)-f(n-1)]/2(The easiest difference format forward, backward, and three kinds of centers. Forward differencing: f (n) = f (n+ 1)-f (n) Backward difference: f (n) = f (n)-f (n-1) Central difference: f (n) = [f (n+ 1)-f (n-1)]/2)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )

1.本程序功能包含显示直方图，OTSU的单值分割，OTSU的二值分割,OTSU的三值分割 2.在OTSU的分割时，通常情况下我们是通过类间方差得到一个分割阈值，但是有时候我们需要得到各灰度级别的目标，那么采取多值分割是 很必要的，通过改进得到OTSU的多值分割方法 3.本程序另外还有显示直方图，计算程序运行时间，处理结果以新的窗口显示等细节，这部分适合MFC初学者。(1. This procedure shows the histogram feature, OTSU single segmentation, OTSU binary segmentation, OTSU three segmentation 2. OTSU segmentation, it is usually the case we are through the between-cluster variance by a partition threshold , but sometimes we need to be the gray level of the target, then the multi-segmentation is necessary, through improved access to multi-OTSU segmentation methods 3. This procedure in addition to show the histogram to calculate run time, treatment the results of a new window display the details of this part of MFC for beginners.)