PAPER11

说明：  基于Matlab的系列图像处理和显示结果程序，采用主成分分析算法，假定载入系列图像数据后可直接运行得到结果。(Matlab based on a series of image processing and display the results of the procedure, using principal component analysis algorithm, it is assumed that loading series of image data can be directly after the operation results.)

运用susan算法进行角点检测，并在图中显示出来(Application susan corner detection algorithm and displayed in the drawing)

SIFT代码，多用于图像拼接，匹配精度高(sciences code used for image stitching, matching high accuracy)

图形图像H264插值算法，应用于图像视频处理(H264 graphic image interpolation algorithm is applied to image video processing)

把一幅图像进行经验模式分解，结果为不同频率分量的IMF，用于希尔伯特黄变换，图像去噪等(empirical mode decomposition)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SP算法，对256*256的lena图处理，比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间(Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and SP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

说明：  对图像进行行列循环加密，采用的是3维混沌系统来增强图像加密的安全性(The 3-D chaotic system is used to enhance the security of image encryption)

均值滤波，高斯滤波，双边滤波以及求PSNR的基本算法(Mean filtering, Gaussian filtering, bilateral filtering and basic algorithms for PSNR of)

otsu算法实现，可以自适应获取阈值，对图像进行二值化。里面配有图像均衡化的程序一起使用，效果更好(otsu algorithm, you can get adaptive threshold for image binarization. Which is equipped with image-based program used in conjunction with a balanced, better)

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.)