VisualSFM_windows_cuda_64bit

用halcon编写的代码实例，用来给初学者学习(Use halcon code examples, for beginners learning )

说明：  在VC++下基于MRF来进行的图像分割源代码(In VC++ For MRF-based image segmentation source code)

本程序包括图像的几何变换、图像的灰度变换、图像的空域增强、图像的频域增强、图像的形态学处理、图像的分割和区域增长、还有图像的数字识别等一系列有关图像的基本操作和处理。(This procedure including the image geometric transform, the image gradation transformation, the image air zone strengthens, the image frequency range to strengthen, image morphology processing, the image division and the region grows, also has the image digital recognition and so on a series of related image elementary operation eo and processing. )

经典的文本水印算法，实现了别人实现不了的功能，希望大家有所用！(The classic text watermarking algorithm to achieve the functions of others fail to realize, I hope you all have a use!)

实现对鱼眼图像的校正 我自己执行过 可以使用(it can realise the rectification of the fish eye image )

自主选择性的查看一幅图像中的部分区域的颜色直方图：允许用户在一幅图像中选择一个矩形区域，通过按住鼠标键画一个矩形，当鼠标放开是，高亮显示矩形框，同时在另一个独立的窗口中，使用画图函数画一个图表（选定区域的颜色直方图），分别用蓝，绿，红表示选中区域中每种颜色的像素数量。(Self-selective color in an image to view a partial area of ​ ​ the histogram: Allows the user to select a rectangular area in an image by holding down the mouse button to draw a rectangle when the mouse is released, the highlighted rectangle, Meanwhile in a separate window, using the drawing functions to draw a chart (selected color histogram region), respectively, with blue, green, red indicates the number of pixels in the selected area of ​ ​ each color.)

说明：  锐化图片 拉普拉斯算法 两种方法 一种是自己写的一种是用了matlab里面自带的函数(ruihua picture Laplace there are two mechof a fi [as fs aflasl;nvb)

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)

Shap Context based Shape recognition

一种快速图像阈值分割法，不是otsu或迭代法，Wellner 1993快速自适应的图像二值化方法，有具体使用方法，很详细(A fast image thresholding segmentation method, not otsu or iterative methods, Wellner 1993 fast adaptive image binarization method)