145

Keystone Transform in ground moving target detection，it s very useful new comer(Keystone Transform in ground moving target detection)

这是图象缩放的代码，对于学习图像的朋友应该有所帮助(This is the image scaling code for the study should help the image of a friend)

显示处理后的BMP图像文件，VC C++ (read the bmp images )

超像素分割程序，既有原码，也有可执行程序，验证过，好用。(Super-pixel segmentation procedure, both the original code, there are executable programs, proven, easy to use.)

M估计子，计算机视觉大牛Peter Meer出品。(Generalized Projection based M-estimator C++ code to find the robust estimate derived without using any user supplied scale. The theory is described in Generalized Projection Based M-stimator. )

对比度拉伸，对原始图像进行对比度拉伸，从而得到拉伸后的图像(tensile contrast, the original image contrast stretching, and thus drawing the images)

在分析聚束合成孔径雷j盘(SAR)方位向时频关系的基础上，提出了一种新的全孔径分辨率SAR成像算法。 根据SAR在条带与聚束模式下回波信号之间的差别，利用现有成熟的条带SAR成像算法实现先对聚束SAR各方位向子 孔径成像，而后再将各方位子孔径带宽合成为全孔径带宽，以形成具有全孔径分辨率的SAR图像，极大降低了系统PRF 的要求。(In the analysis of spotlight synthetic aperture radar dish j (SAR) azimuth based on the time-frequency relationship, a new full-aperture resolution SAR imaging algorithm. According to SAR in strip and spotlight mode, the difference between the echo signal, the use of existing mature band SAR imaging algorithm first bit of the spotlight SAR the parties to the sub-aperture imaging, and then place then all the bandwidth synthetic aperture the bandwidth for the full aperture to form a full aperture resolution SAR images, greatly reducing the system PRF requirements.)

DIX公式优化求解层速度，使得速度变得更加平滑。(DIX formula optimization solution velocity, making speed becomes smoother.)

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.)

三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)