超像素分割程序

说明：  以python语言为平台，对机载激光雷达原始数据Las格式和txt格式进行读取，并根据信息熵确定维度特征计算的最佳邻域半径，最后计算得到最佳邻域半径和最佳维度特征值(Based on Python language, the Las format and TXT format of airborne lidar raw data are read, and the optimal neighborhood radius is determined according to the information entropy. Finally, the optimal neighborhood radius and the optimal dimension eigenvalue are calculated.)

说明：  在大图中寻找到小图。 并圈出小图所在位置。 图片用picture控件显示。(In the big picture to find a small map. )

计算图像的质心，用图像的算发进行操作。这样可以有效的算出图像的质心(The centroid of the calculated image, with an image of the operator hair operation. This can effectively calculating the image centroid)

这次上传的代码是关于MRI Brain Scan_segmentation用的代码，希望能对大家有用(this code is uploaded on Scan_segmentation Brain MRI of the code, we hope that the useful)

天正T20 4.0（2017）版本过期注册机、补丁(Tian Zheng T20 4 (2017) version software and expired registration machine, patch)

图像金字塔，得到降采样后的图像和残差图像(image pyramids,generating image pyramids and predicton residual)

SAR图像和光学图像的配准算法！！！matlab源码~~~~绝对好！(SAR image and optical image registration algorithm! ! ! matlab source code ~ ~ ~ ~ definitely good!)

基于机器视觉，控制三自由度运动控制台来调整显微镜位置，通过图像处理得到易拉罐的表面刻痕大小和长度。(The software control the motion platform with 3 d.o.f. to position the microscope and then test the notch of cans using image processing.)

Doris整个工程文件，包括利用Doris进行影像配准，干涉图生成，DEM生成，是对SAR影像干涉处理的绝佳代码(Doris whole project documents, including the use of Doris for image registration, interferogram generation, DEM generation, is an excellent SAR image processing code for intervention)

针对电气设备同一场景的红外与可见光图像间一致特征难以提取和匹配的问题，提出了一种基于斜率一致性的配准方法。首先通过数学形态学方法分别提取红外与可见光图像的边缘，得到粗边缘图像；然后通过SURF算法提取两幅边缘图像的特征点，根据正确的匹配点对之间斜率一致性的先验知识，进行特征点匹配；最后通过最小二乘法求得仿射变换模型参数并实现两幅图像的配准。实验结果表明，该方法有效提高了匹配点对的正确率，能够对电气设备红外和可见光图像实现高精度的配准。(In this paper, a method based on slope consistency is proposed to solve the problem that the uniform features of infrared and visible images are difficult to extract and match. Firstly, the edges of infrared and visible images were extracted by mathematical morphology, and the rough edge images were obtained. Then, the feature points of two edge images were extracted by SURF algorithm, and the priori knowledge of slope consistency was obtained according to the correct matching point, and the feature points were matched. Finally, the model parameters of affine transformation are obtained by least square method and the registration of two images is realized. The experimental results show that this method can effectively improve the accuracy of matching points, and can realize high precision registration of infrared and visible images of electrical equipment.)