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一个很好类子来说明类模板的使用，可以比较明了类模板的使用方法，最好进行单步调试(use of class template )

说明：  通过matlab，可以快速得到图像的信噪比，峰值信噪比等。里面也有一些解释(Through MATLAB, the signal-to-noise ratio and peak signal-to-noise ratio of image can be obtained quickly. There are also some explanations)

Assembly code for interfacing a 7segment that display a random Letters.

用于植物叶片分类的图形界面,是Matlab源码，用BP神经网络实现(For plant leaf classification graphical interface, the Matlab source code, using BP neural network)

说明：  这是基于matlab的一个视频控制程序，他可以一次控制10个摄像头，并可将其拍照的程序进行保存(This is a video based on matlab control program, he could control the camera 10, and the procedure to save the pictures)

用python编写的基于CV2的图像融合程序，实现偏振信息到IHS的转换、融合。(Using python to write the CV2-based image fusion program to achieve the polarization information to IHS conversion, integration.)

说明：  注：运行前自己准备图像，修改I = imread('')单引号中的路径 1. 给一张灰度图添分别添加高斯噪声和椒盐噪声。然后分别进行均值滤波和中值滤波，并把处理后的图片保存。 2. 图像频域处理，对图像进行傅里叶变换，进行高通滤波，显示处理结果。 3. 图像平移和旋转 4. 分别用OSTU阈值法和全局阈值法分割图像，然后进行腐蚀和膨胀处理，显示处理结果。(Note: Prepare the image by yourself before running, modify the path in single quotes of I = imread('')1. Add Gaussian noise and salt and pepper noise to a grayscale image. Then perform mean filtering and median filtering respectively, and save the processed pictures.2. Image frequency domain processing, Fourier transform of the image, high-pass filter, and display the processing result.3. Image translation and rotation4. Separate the image with OSTU threshold method and global threshold method, and then perform corrosion and dilation processing, and display the processing results.)

opencvsharp检测圆

图像拼接的matlab实现，内含有算法的设计文档和源程序等(Matlab implementation, image stitching algorithm design documentation and source code contains)

1.从图像中识别几何形状的基本方法；基本原理是在于利用点与线的对偶性，将原始图像空间给定的曲线通过曲线表达形式变为参数空间的一个点，这样就把原始图像中给定的检测问题转化为寻找参数空间的峰值问题。 2.基于Gabor变换的人眼检测MATLAB实现。 3.基于Radon的车牌矫正方法。 4.基于模型函数headata产生不同大小的头模型数据。 5.基于函数wdcbm2()设置图像分层阈值压缩参数，实现图像压缩(The basic method of identifying geometry from the image; the basic principle is to use point and line duality, the original image space curve given expression through the curve into a parameter space of a point, so the peak detection problem is given in the original image is transformed into searching the parameter space.)