AdaBoost_TrainData

数字图像处理的基础知识学习 涵盖源代码，有助于提高编程(digital image processing )

使用matlab来实现图像的处理，比如直方图均衡化等等(Using matlab to achieve image processing, such as histogram equalization)

实现上位机对wifi智能车的控制，接收路由器传输图像并显示界面是用Qt4.0编写，开发环境QtCreater 图像处理Opencv(Host computer for wifi smart car control, image transmission and display the receiving router)

该算法实现车牌快速自动识别,建立了车牌字符库和车牌图象,可以直接运行(The algorithm quickly realize automatic license plate recognition, license plate character set up libraries and license plate images can be directly run)

说明：  jm软件源码，用于流媒体编码标准H264的性能评测(jm-source software for streaming media encoding standard of the performance evaluation H264)

代码实现2014ICML文章Motion blur kernel estimation via salient edges and low rank prior，基于显著边缘和低秩先验的运动模糊核估计。(The code implementation of Motion blur kernel estimation via salient edges and low rank prior)

苹果在线检测系统，实时性比较好，可用性更好，很好很好(Apple' s online testing system, real-time is better)

启动电脑外接USB摄像头， 完成的1-5数字手势识别(Start the computer to connect the USB camera to the 1-5 digital gesture recognition)

说明：  获取图像灰度共生矩阵特征值代码，包括能量、熵、惯性矩和相关度的均值和标准差，已测试，代码及注释清晰。(The eigenvalue codes of gray level co-occurrence matrix, including the mean and standard deviation of energy, entropy, moment of inertia and correlation, have been tested and the codes and annotations are clear.)

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.)