MatlabMedialAxis-Ver1.0

说明：  很好地实现了激光雷达点云的ICP配准，附带数据(ICP registration of LIDAR point cloud is well realized)

机动目标跟踪，交互式多模型算法的代码，一个简单的例子(Maneuvering target tracking, interactive multiple model algorithm code, a simple example)

织物疵点检测和识别系统.采用2中方法，有gui界面，支持实际纺织物的检测(Fabric defect detection and identification systems. Using 2 methods, gui interface, support for the detection of the actual textile)

Random Walker图像分割Matlab源代码，需要安装图形分析工具箱，地址http://eslab.bu.edu/software/graphanalysis/，给出了一个应用例子。(Random Walker Image Segmentation Matlab source code, need to install the graphical analysis toolbox, address http://eslab.bu.edu/software/graphanalysis/, gives an application example.)

说明：  通过比较在预处理中选择了灰度变换增强、选择了中值滤波平滑、Sobel算子锐化并利用Ostu法阈值分割法进行分割；用了小波多尺度检测进行检测，最后计算出大桥裂缝的相关数据，在大桥桥梁裂缝检测方法与研究中可以较好的实现大桥桥梁裂缝图像的检测。(the gray-scale transform enhancement, median filter smoothing, Sobel operator sharpening are selected in the preprocessing, and the Ostu threshold segmentation method is used for segmentation; finally, the wavelet multi-scale detection is used for detection, which can better realize the detection of the bridge surface crack image in the bridge surface crack detection method and research.)

matlab源文件，可以实现利用matlab来画各种饼图，显示直观，对做数据分析和论文的同志会有帮助。(matlab source files, can be achieved using matlab to draw a variety of pie, intuitive display, and to do data analysis and papers comrades would help.)

这是我本科的毕业设计做的关于医学图像的基于形状的图像检索。预处理用小波去噪 ，特征选用不变矩。(This is my undergraduate graduation design done on the medical images based on the shape of the image retrieval. Pretreatment with wavelet denoising, feature selection invariant moment.)

在ycbcr颜色空间分割火焰，并找到火焰质心(Split the flame in the YCbCr color space and find the center of mass of the flame.)

基于双目视觉的三维重建，包含了重建过程中的基本步骤。(Binocular vision-based three-dimensional reconstruction, including the reconstruction of the basic steps of the process.)

说明：  可以实现旋转，错切，各种投影图，透视图。是学习三维变换很好的原始参考资料(Can rotate, shear, a variety of projection, perspective. Is to learn the original three-dimensional transform good reference)