新建文件夹

在qt5上实现图像处理界面的设计，实现基本的图像处理算法，结合opencv(Implement image processing and interface design on qt5, to achieve the basic image processing algorithms, combined with opencv)

Viola-Jones Fast Face Detection的matlab实现。(Viola-Jones Fast Face Detection Mex Implementation)

医学图像处理方面的一些基础算法，VC环境下编译的一个非常好的程序框架(Medical image processing, some of the basic algorithm, VC compiled under the framework of a very good program)

主要是关于模糊熵的，里面还和灰色关联度有点联系。搞图像的应该可以看的懂的。(Mainly on fuzzy entropy, which has little contact and the gray. Should be able to read out the image of understand.)

BP神经网络图像分类程序,代码很好懂，适合初学者。样本选的不是很大。(BP neural network image classification procedures, code easy to understand for beginners. Sample selected is not great.)

图像去噪，小波硬阈值滤波，sym4小波基函数(Image denoising and hard threshold wavelet filtering, sym4 wavelet basis function )

一个老外用matlab 编写的5点法图像相对定向程序，是目前最新的算法实现。(5 point to prepare for a foreigner relative orientation program)

图象数据透明化源码，主要使用两种方法使图象透明显示：一种是用画板载入画图片，画出方式选择透明白色；另一种是将图象数据字节集化，并按高度和宽度逐一改变颜色使颜色透明。(Transparent image data source, the main use of the image transparent display two methods : one is to use Sketchpad loaded Picture , Draw Mode Select transparent white; the other is a set of bytes of image data , and press)

椭圆、圆、矩形等的识别，在MATLAB下运行.(Ellipse, round, rectangle 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.)