dongtai

本书是国内第一本全面介绍OpenCV的中文版图书，本书可作为信息、图像处理和模式识别等领域的高年级本科生、研究生使用，也可作为研发人员的参考手册。(This book is the first comprehensive introduction to OpenCV s Chinese version of the book, this book can be used as information, image processing and pattern recognition in areas such as advanced undergraduates, graduate students, but also as a reference manual for researchers.)

说明：  基于mmdetection框架的深度学习目标检测框架(deep learning object detection based on mmdetection)

说明：  基于Landsat8卫星影像数据，在GEE平台中的监督分类(Supervised classification based on landsat8 satellite image data in GEE platform)

能够得到二维、三维voronoi图，修改后可以获得任意数量的voronoi图。(The two or thress-dimensional Voronoi diagram can be obtained, and any number of Voronoi diagrams can be obtained after modification.)

通过matlab自带函数等，实现复杂背景下，提目标外轮廓，并且去除内部小区域，得到边缘(By using MATLAB functions and so on, the target contour can be extracted under complex background, and the inner small area can be removed to get the edge.)

说明：  适用于任意图像中轮廓的直线段检测与描绘，对于公路车道直线检测具有良好效果。(Linear segment detection and description for contours in arbitrary Images,it has good effect for straight line detection of highway lanes.)

六次迭代使三角形三条线分形折叠，最后组成雪花(Six iterations make triangles become snow)

使用二維的傅立業轉換，在以反傅立業的方式，將結果以影像話方式呈現。(Industry use of two-dimensional Fourier transformation, anti-Fu Li industry in the way of results, then the way to show images.)

说明：  数字图像处理 小波处理 ，去噪音，比基于傅里叶变换的去噪方法好。 含噪信号经过预处理，然后利用小波变换把信号分解到各尺度中，在每一尺度下把属于噪声的小波系数去掉，保留并增强属于信号的小波系数，最后再经过小波逆变换恢复检测信号。(The noisy signal is pre-processed, then the signal is decomposed into different scales by wavelet transform. The wavelet coefficients belonging to noise are removed at each scale, and the wavelet coefficients belonging to the signal are retained and enhanced. Finally, the detected signal is restored by inverse wavelet transform. It is better than the denoising method based on Fourier transform.)

改进后的pca用于故障检测与诊断，应用于TE的化工过程，仿真结果比传统的pca方法效果更好(Pca for improved fault detection and diagnosis of chemical process applied to TE, the simulation results than the traditional method pca better)