Compressive_Sensing

数字图像处理，K-SVD字典学习方法，信号的稀疏与冗余表示理论，图像压缩，图像去噪(Digital image processing, K-SVD dictionary learning methods, sparse and redundant signal representation theory, image compression, image denoising)

说明：  自己编的SVM代码，适合初学者，多多关注，，(SVM learning, suitable for beginners, rich code, SVM code, suitable for beginners, more attention)

图像增强里的小程序，有中值滤波，平滑处理还有频域增强(Image Enhancement in the small procedures, median filtering, smoothing also enhanced the frequency domain)

图像格式转换例程源码，程序主要结合易语言图像格式转换支持库，可以将某些图象文件的格式转换到其它图象文件的格式(Image format conversion routines source, the program mainly with easy language support library image format conversion, you can convert the image file format to some other image file formats)

VC++,DirectX,运动图像检测,运动轨迹跟踪(VC++, DirectX movement trajectory tracking image detection)

基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理 (Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixels, that is, to converge to the same point as a starting point to the category and then assigned to this type of labeling of these starting points, at the same time to contain too few pixels to remove the category. Then, using thresholding segmentation method of binary image processing)

基于图像对比度和波段相关性的波段选择算法，适用于高光谱图像的特征选择(Bands selection algorithm based on image contrast and correlation of bands for hyperspectral image feature selection)

三自由度并联机器人工作空间求解matlab程序，用球坐标分割的空间(workspace for solution)

这个压缩包中包含了巴特沃斯滤波算法的高通和低通两款代码，同时还有几篇巴特沃斯算法的文章，记录了关于巴特沃斯算法的一些应用领域。(This compressed package contains the code of two of the high-pass and low-pass Butterworth filter algorithm, at the same time, there are few Butterworth algorithm articles recorded Butterworth algorithm applications.)

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.)