资源列表
Image Noise Level Estimation by Principal Component Analysis
- 论文Matlab实现:S. Pyatykh, J. Hesser, and L. Zheng, "Image Noise Level Estimation by Principal Component Analysis", IEEE Transactions on Image Processing, Volume: 22, Issue: 2, Pages: 687 - 699, February 2013.
基本数字水印和DCT数字水印
- 掌握基本数字水印和DCT数字水印的实现和检测方法;检验DCT数字水印,抵抗高斯噪声攻击、椒盐噪声攻击、剪切攻击和旋转攻击的能力。(Grasp the implementation and detection methods of basic digital watermarking and DCT digital watermarking, and test the ability of DCT digital watermarking to resist Gauss noise attack,
warp
- Python语言实现全景图拼接,识别特征,ransac提取,后拼接全景图(Python language achieves panorama mosaic, recognition features, RANSAC extraction, and then mosaic panorama)
二维码扫描识别
- 基于opencv+zbar+visual studio的二维码扫描识别,文件里有写具体做法和具体代码(opencv ,zbar,visual studio)
5
- python实现双目立体匹配SGBM算法(SGBM algorithm for binocular stereo matching based on Python)
CS_Lenabmp
- BCS代码稀疏重构Lena.bmp图像,包中含有代码和图片本身,题主给了一定注释,便于CS初学者学习(BCS code sparsely reconstructs Lena. BMP image. The package contains code and image itself. The theme is given a certain comment, which is convenient for CS beginners to learn.)
石鑫华视觉网-中科院自动化所 计算机视觉课件
- labview基础视觉识别的东西,讲的很细(The basic visual recognition course of LabVIEW is very detailed.)
VMD_tu
- vmd分解之后分解信号和原信号的对比图、频谱图等(After VMD decomposition, the contrast and spectrum of the decomposed signal and the original signal, etc.)
生物特征识别技术手指静脉识别技术_余成波
- 结合图像处理MATLAB的基本原理和知识,详细介绍了手指静脉预处理,图像分割,静脉纹路分割,滤波去噪,细化等相关内容。(Combined with the basic principle and knowledge of image processing, this paper introduces the finger vein pretreatment, image segmentation, vein vein segmentation, filtering and denoising,
分布式tensorflow
- 1.使用distribute.py在分布式tensorflow中进行训练mnist模型 2.使用mnist_test.py进行测试模型,获取输出结果(1. Training MNIST model in distributed tensorflow using distribute.py 2. Use mnist_test.py to test the model and get the output results)
NDVI批量计算Python代码
- NDVI最大值合成法批量计算Python代码(Python Code for Batch Computing by NDVI Maximum Composition Method)
matlab命令流
- 信号处理傅里叶变换,三分之一倍频程,荷载时程曲线绘制(Signal processing Fourier transform, one third octave, load time history curve drawing)