histogrameq

稀疏矩阵的实例，讲述如何用稀疏矩阵进行实际的编程。本例是以超市购物为实际背景。(Examples of sparse matrix)

多边形填充作业.rar(filled polygon operations. Rar)

用于摄像机标定的Harris角点检测代码码。能达到亚像素精度。压缩包内附说明文档。 (Harris corner detection for camera calibration code code. To achieve sub-pixel accuracy. The compressed package included documentation.)

光斑中心提取算法，在VS2010下，用opencv2.3.1编写的控制台程序，主要功能是利用hession矩阵和高斯滤波进行光斑中心点提取，里面附测试图片，提取精度为亚像素级别。(Spot center extraction algorithm, in VS2010, written by opencv2.3.1 console program, the main function is to use hession matrices and Gaussian filter for extracting the center spot, which is attached test images, the extraction accuracy of sub-pixel level.)

简单的图形文字识别软件，希望大家喜欢我的分享，谢谢(Simple graphical text recognition software, I hope you like my share, thank you)

hilbert程序实现变换 需要的朋友可以参考(hilbert transform program can refer to a friend in need)

C++和halcon结合，用于图形处理时进行三相机标定(C++ and halcon combined for three camera calibration)

模式识别的Sobel和Kirsch边缘检测程序(Sobel and Kirsch edge detection algorithm of Pattern recognition)

摘　要:舌象中的裂纹是中医舌诊中的重要内容。由于拍摄条件的不同,舌象质量有较大差异,传统的阈值选取方法并不 适用。文中提出一种舌象裂纹检测的自适应阈值选择方法。该方法利用舌象的L 3 a 3 b 色彩特征及区域的分裂- 合并, 对舌象进行区域分割,自适应地选取舌中部区域的色彩值作为阈值,对整个舌象进行裂纹提取。经验证,本方案对不同的 舌象能较好地提取出裂纹,实现舌象裂纹诊断的客观化。(Tongue’ s crack is a very important part of herbalist doctor diagnosis. For the different screen conditions , there are many differ2 ences on the tongue’ s quality. It is not effective to use the t raditional threshold ext raction algorithm. In order to solve this problem , pro2 pose a new adaptive threshold algorithm. Present the segmentation of the image of tongue by using the feature on tongue in the L 3 a 3 b color space and the split- combining algorithm. Then use the threshold , which is the color value ext racted automatically in the middle of the tongue , to detect the crack of the whole tongue. The experiments show that this approach is able to distill the crack effectively to different tongue images , and achieve the objectivity of the diagnosis based on the tongue’ s crack.)

对图像进行镜像处理，是最基本的图像处理操作(Mirror image is the most basic image processing operations )