信号

说明：  通过用户给定的标签信息，来提取图像中的标记像素点位置，保存为种子点(Through the label information given by the user, the position of the label pixels in the image is extracted and saved as seed points.)

说明：  图像配准SIFT算法,使用python实现（github搬运）(image resigtration sift)

带通配符的字符串匹配,多重匹配,支持通配符“*”，“？”(String matching with wildcards, multiple matches, supports wildcards " *" , " ?" )

基于红外图像的弱小目标检测与跟踪:计算图像的局域灰度概率矩阵,在图像上标出检测到的奇异点,标出最终检测到的小目标(Weak target detection and tracking based on infrared images: calculate the the local grayscale image probability matrix, detected singular points marked on the image, marked the final detected small goals)

Information of transform watershed

说明：  利用马尔可夫模型检测分割图像，获得MAP准则下的图像分割结果(??????????í ?? ?? ?? ?? ò?? ?? ???? ?? ì ?? ?? ?? ?? ???? ?? ???? ?? ?? ????μ??MAP × ?? ??ò????μ???? ?? ???? ?? ?? ?? ?? á ?? ??)

多边形填充 多边形裁剪 自定义多边形区域 自定义裁剪区域(Polygon fill polygon clipping custom polygon area custom crop area)

通过统计直方图并作相应的处理完成了红外图像非均匀性校正，特别是针对红外图像中常见的条纹非均匀性(Through statistical histogram and processed accordingly infrared image non-uniformity correction, especially for the common striped infrared image non-uniformity)

利用改进的Sobel算子检测图像边缘,从水平、垂直、左右对角线的方向计算Sobel梯度，并且加入拐角的梯度值。(The improved Sobel edge detection operator, horizontal, vertical, left and right diagonal direction Sobel gradient calculation, and adding the value of the gradient of the corner.)

我写的贝叶斯fusion的Visual C+++源码，简明易懂，提供参数说明 可直接使用。 (The Bayesian fusion the Visual C+++ of what I wrote source, easy-to-understand parameters can be used directly.)