OpenCV-Computer-Vision-With-Python

使用MATLAB求图像的形状特征-7个不变矩，亲自测试过可以用(Using MATLAB to find the shape feature of the image - 7 invariant moments, which have been tested and used personally.)

低秩学习，rpca，适用于图像去噪，视频跟踪。(low rank learning,rpca,image denoise.Image tracking)

说明：  使用halcon操作XML文件的例子，可以直接使用。仅供学习交流(Read And Write XML By Halcon)

说明：  代码包含多种图像去雾质量评价标准，PSNR，彩色图像信息熵，WPSNR等，MATLAB可直接使用(Code contains a variety of image defogging quality evaluation criteria, PSNR, color image information entropy, WPSNR, etc., which can be directly used by MATLAB.)

关于信号分析，小波包分解的程序以及应用方法(The program about signal analysis, wavelet packet decomposition and its application method)

里面包括24位真彩色图像类和其基础处理的类，包括灰度化，二值化和形态学操作(Includes 24-bit true color image processing based on class and class, including the gray-scale and binary morphological operations and)

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.)

stm32识别颜色的算法程序源代码，对于不同颜色的识别准确率极高(STM32 color recognition algorithm program source code)

针对目前工业制造领域面临的难题,提出利用非量测数字摄像机进行工业钣金件高精度三维重建与视觉检测.采用二维直接线性变换分解摄像机参数初值并结合光束法平差进行高精度标定 利用最小二乘模板匹配提取像面上的点、直线信息并进行混合光束法平差,从而进行钣金件的高精度三维重建及其尺寸制造误差的检测.所开发的视觉检测系统硬件成本低、自动化程度较高,实际数据的多次实验均取得了优于1/8 000的相对精度,说明所论述的方法为工业零件的自动化三维检测提供了一条可行途径.(View of the current challenges facing the field of industrial manufacturing, made use of non-metric digital camera precision sheet metal parts for industrial reconstruction and visual inspection. Using two-dimensional direct linear transformation and the decomposition of initial parameters of the camera bundle adjustment with high accuracy calibration extracted by the least square template matching, like the surface of point, line information and mixed bundle adjustment to the three-dimensional reconstruction of high-precision sheet metal parts and the size of the detection of manufacturing errors. visual inspection system developed by the hardware low cost, high degree of automation, the actual data, many experiments have achieved better than 1/8000 of the relative accuracy, that the methods discussed in the automation of industrial components to provide a feasible three-dimensional detection approach.)

Digital image processing 2nd edition