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图像的平滑函数，对指定区域的图像，并有门限值和魔板大小限制。(the smooth of a bmp image)

基于图像处理，vc++平台的水果识别。通过亮度调整，梯度处理，边缘图像，边界跟踪，计算颜色形状等，计算出结果。(Based on image processing, fruit recognition vc++ platform. Through the brightness adjustment, gradient, edge image, boundary tracking, calculation of color shape, the calculated results. )

刚体配准方法中，通常使用互信息函数作为相似性测度，此类提供计算的封装。(rigid registration method, which is commonly used as a mutual information function similar measure, the provision of such calculation package.)

VisualC++数字图像处理技术详解第2版光盘-第六章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter VI)

数字图像处理中常用的标准图像，包括大家所共知的lena等图片的高分辨率版本，推荐大家下载(Commonly used in digital image processing standard images, including all the known to the lena and other high-resolution version of the picture, we recommend downloading)

多边形的合并算法，该算法能够实现任意两个多边形（包括凹多边形）的合并.( The polygon merge algorithm, this algorithm can realize wilfully two polygons (including concave polygon) merge )

kinect扫描到的人体分离出来，并以二值图显示，且可以保存在本地，丰富的注释帮助理解(kinect body scan to be separated, and binary image display and can be saved locally, rich annotation to help understand)

填充区域图像，设置填充区域，填出图像的颜色(Filled regional images, set fill area, fill in the color of the image)

说明：  基于VC++的图像的DFT变换和DCT变换，并可实现两者间的对比转换(VC++ based on the image of the DFT transform and DCT transform, and to achieve the contrast between the two conversion)

EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)