MyLT

说明：  将原图二值化处理后，对二值图像进行腐蚀处理去掉图中主体以外的噪点或小斑迹(After binarization of the original image, the binary image is corroded to remove the noise or small spots outside the main body of the image)

基于大津法和迭代法的图像二值化程序，相当于利用该方法进行了图像的分割，源码已调试，很好使！(Based on the Otsu method and iterative method of image binarization procedure is equivalent to using the method of image segmentation, source code has been debugging, very good to make!)

此matlab程序用于横向和纵向都被剪碎的纸片的拼接复原，高质量(This matlab program is used for the stitching of both horizontal and vertical pieces that are cut and broken, and of high quality)

将一个灰度图像分解成为八个位平面 然后再合并起来 可以对每一个位平面实现不同的处理(A gray level image is decomposed into eight bit plane and then combined to each bit plane to achieve different treatment)

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).)

说明：  k-means,DB-SCAN,基于密度峰值的聚类算法的matlab简单实践Clustering-matlab-master(k-means,DB-SCAN,Clustering-matlab-master of Simple practice of matlab based on density Peak clustering)

说明：  基于内容的图片搜索，基于java实现，图片搜索及图像相似度计算(Content-based image retrieval)

本例功能是显示一个由反走样处理的线段组成的八面体(functions of the cases showed an anti-aliasing by the Line of octahedral)

正则化工具,完整的正则化工具，包括各种正则化的处理，使用matlab编程语言(regularization tools)

说明：  本文简要回顾了OpenCV的发展，介绍了OpenCV的各种应用，并详细分析了其中一些应用的原理、算法和代码，最后还介绍了一些关于OpenCV的最新研究进展。(This paper reviews the development of OpenCV, OpenCV introduces a variety of applications, and application of a detailed analysis of some of the principles, algorithms and code, and finally describes some of the latest research on the progress of OpenCV.)