jbigkit-2.1

说明：  机器视觉中三维重建的源码 具体包括4个文件，其中.c文件用于产生图像特征点矩阵(In the three-dimensional reconstruction process, resulting in the source image feature dot matrix)

用于在图像处理以及计算机视觉中，作为测试的COIL20数据集(The COIL20 data set used as a test in image processing and computer vision)

包含了主要的图像质量评价的matlab源程序。图像熵，图像的方差和，以及基于边缘检测算子的质量评价函数。(Contains the main image quality evaluation of the matlab source code. Image entropy, variance and images, as well as edge detection operator based on the quality of the evaluation function.)

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).)

python实现人脸识别，通过pca进行降维然后使用SVM算法进行分类(Python realizes face recognition, dimensionality reduction through pca and classification by SVM algorithm)

说明：  这个文件中包含了三种图像相似度的计算方法，分别是灰度分布计算方法、颜色分布直方图计算方法、还有结构相似度SSIM计算方法。(This file contains three image similarity calculation methods, namely, gray distribution calculation method, color distribution histogram calculation method, and structure similarity SSIM calculation method.)

显示JPEG图片的源码.........................................(show jpeg picture)

s世界计算机视觉三大会议之CVPR会议，中国人发表的经典立体匹配的论文(s World Computer Vision Conference CVPR three meetings, the Chinese classic stereo matching published papers)

首先将非平稳的故障振动信号进行双树复小波包分解，得 到不同频带的分量；然后对每个分量求其峭度值和相关系数并进行比较；最后选取峭度值和相关系数较大的分量 进行软阈值降噪和双树复小波包重构，即可有效地消除振动信号中噪声的干扰，同时保留信号中的有效信息即实 现了故障特征信息的提取。(In view of the above situation, a new fault diagnosis method is proposed based on dual-tree complex wavelet packet transform and threshold de-noising. Firstly, the non-stationary fault signal is decomposed into several different frequency band components through dual-tree complex wavelet packet decomposition. Secondly, Kurtosis and the cross-correlation coefficient of each component are obtained and compared. Due to the kurtosis reflecting the signal variations, if the kurtosis value is bigger, the degree of the change of signal is bigger too. The correlation coefficient can reflect the proximity between the component and the original signal at the same time, the correlation coefficient is bigger, the more similar with the original signal. Finally, the components that have a bigger value are chosen to be de-noised by a soft threshold and reconstructed by dual-tree complex wavelet packet transform. The noise interference was eliminated effectively, and the effective si)

基于matlab的三维重建代码，提取两幅图片的匹配点(Based on three-dimensional reconstruction of the matlab code, extract the two images match point)