layerstacking_ding

基于Sift算法开发商标图案匹配程序，(1)各个CBIR模块及其技术； (2)Sift算法及其在图像匹配、目标识别中的应用； (3)Sift算法在图像检索中的应用。 (Sift algorithm development based on pattern matching program mark (1) Each CBIR module and its technology (2) Sift algorithm and its application in image matching, object recognition application (3) Sift algorithm in image retrieval applications.)

图像增强的算法，但是有一定的局限性……但是对于一般比较典型的问题效果还是不错的……(Image enhancement algorithms, but there are some limitations ... but for a typical effect is good ...)

canny边缘检测，还有车道线检测。效果还可以。(canny edge detection, and lane detection. The effect can be.)

VC图像编辑软件 就要卷积模糊和卷积模糊加噪 以不同方法（大于等于2）实现图像的模糊效果（局部或全局）。并以BMP格式存储。(soft)

针对大尺度图像配准和不同传感器图像配准问题, 介绍了一种基于 SIFT 的图像配准方 法。(For large-scale image registration and image registration problem from different sensors, introduces a SIFT-based image registration method.)

视频编解码技术，在有限带宽的情况下，能够大幅度的压缩编码比特数，图像的质量没有丝毫的下降。(Video codec technology, in the case of limited bandwidth can be substantial coding bits, the image quality is not the slightest decline.)

数字图像处理用到的标准测试图像，很多图片。(IMP test images。)

基于区域的稠密立体匹配方法（论文） 关键词：种子点；区域增长；稠密匹配(Based on Region Dense Three—dimensional Match Method)

说明：  直方图均衡化的一个程序，只能对BMP的黑白图像进行直方图均衡化(A histogram equalization process, only black and white BMP image histogram equalization)

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.)