cseg

说明：  a）针对合成纹理图像（共有4 个合成纹理图像，见文件夹： Texture_mosaic_data）中每一个像素提取纹理特征向量； b）利用聚类技术（推荐用k‐均值聚类，可以从网上查找原码）对特征向 量空间中的点进行聚类，类别数可根据图像中的实际纹理类数确定。最后把类 属标签映射成图像形式显示（如下图，其中b、d、f、h 为相应的基准分割图像）。(A) for synthetic texture images (there are 4 synthetic texture images in total, see the folder: Texture feature vector is extracted from every pixel in texture; B) use clustering technology (K-means clustering is recommended, and the original code can be found on the Internet) to search the feature direction The number of categories can be determined according to the actual number of texture classes in the image. Last class The generic label is mapped to an image (as shown in the figure below, where B, D, F, h are the corresponding reference segmentation images).)

利用matlab 对高速公路的裂缝进行检测，用到了数字图像处理的基本算法(The use of matlab on the highway crack detection, using the basic digital image processing algorithms)

绘制一个小房子，并演示透视变换。(drawing a small house, and demonstrated perspective transformation.)

说明：  实现图像配准，两幅不同来源的数据配准，可直接运行，效果直观(Image registration, two different sources of data registration)

说明：  连通监控视频的 火灾检测，颜色特征，纹理特征等(fire detection Description for each code - extract_red.m, objectlabel.m : Ground Truth labeling automation code generated from Apps - FireDetection.m : MATLAB class to import custom algorithm to Image Labeler - yolov2_detect.m : Entry function for GPU Code generation - GroundTruthTest.mlx : Test code for ground truth labeling - yoloFire_Train.mlx : Training code for YOLOv2 network - Live_Webcam_YOLOv2.mlx : Live webcam demonstration code for YOLOv2 network)

HDMI接口定义，包括a，b，c，d，包括各种接口的详细介绍(HDMI interface definition, including a, b, c, d, including detailed introduction of various interfaces)

说明：  利用图象边缘特征的基于豪思道夫距离的景象匹配算法，匹配速度要比基于灰度的算法高好几个数量级(use of the images on the edge of Ho Sze-Astoria distance from the scene matching algorithm, speed matching gray than the algorithm based on several high-volume)

说明：  对光纤布拉格光栅进行matlab出图，光纤布拉格光栅也称光纤光栅，简称FBG(use matlab to FBG figuer)

说明：  对于一些图像来说，常用的边缘检测算法有时候无法设立合适的阈值将它们的梯度较小的模糊边缘检测出来。为了解决这个问题，有两种解决方法：将图像方差标准化，拉大模糊边缘的梯度值，或通过设置sigmoid函数，将像素所在区域的信息传递到梯度值中去，对其进行调整，就能找到合适阈值，有效地将模糊边缘提取出来。本程序把这两种算法实现并与与传统算法进行了比较。(For some images, the commonly used edge detection algorithm sometimes can not be an appropriate threshold for the gradient of their smaller fuzzy edge detected. To solve this problem, there are two solutions: the image variance standardization, widening the fuzzy edge of the gradient value, or by setting the sigmoid function pixel region to disseminate the message to the gradient values to adjust, on the can find a suitable threshold, effectively extracted fuzzy edge. This procedure of these two algorithms with the traditional algorithm.)

改进分水岭算法，详细注解标在m文件内了，希望对大家有用(Improved watershed algorithm and hope for all of us)