来自西弗吉利亚大学li xin整理的CV代码合集(转)

本文档汇集了计算科学领域的可复现研究成果,包括信号处理、计算机视觉、机器学习等多个方面。提供了丰富的源代码资源,旨在促进科学研究的透明性和可验证性。
部署运行你感兴趣的模型镜像

目录(?)[-]

  1. Image denoising
  2. Image coding
  3. Image demosaicing
  4. Image interpolation and Superresolution
  5. RGBD image processing
  6. Image segmentationparsing and matting
  7. Image deblurring
  8. Blind image deblurring
  9. Texture synthesis
  10. Image inpainting
  11. PDE-based image processing
  12. Image quality assessment
  13. Biometrics
  14. Gradient-domain image processing
  15. Video coding
  16. TextureShapeImage classification
  17. Object detectionrecognition
  18. Image registrationmosaicingOFE
  19. Actioneventscene recognition
  20. Visual tracking
  21. SaliencyObjectness Detection
  22. Low rank methods
  23. Deep Learning
  24. Manifold learning and embedding
  25. Wavelets and frames
  26. Compressed Sensing
  27. HDR imaging
  28. Biomedical Imaging
  29. D Phase Unwrapping
  30. DTI and fiber tractography
  31. Data Clustering
  32. Stereo matching Multiview geometry
  33. Graphics CartoonsMotion3D
  34. Machine learningNeural Networks
  35. Blind source separation
  36. Camera calibration
  37. SamplingPhysics-based Simulation
  38. Evolutionary computingOptimization
  39. Networking Research
  40. Miscellaneous
  41. Links to other communities reproducible research effort
  42. Links to reproducible booksjournalstutorials
  43. Links to other individuals reproducible research

Reproducible Research in Computational Science

 

“It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong” - Richard Feynman

 

"As a method for finding things out, science lives by its disdain for authority and its reliance on experimentation." - Chris Quigg

 

Welcome to this site about reproducible research in computational science (including signal processing, computer vision, machine learning and neural computation). This site is intended to share the source codes of the latest advances in various technical fields to the best of my knowledge. Only throughReproducible Research (RR), can we live up to the standard that hard-core science has established since Bacon and Newton. If you know of any release of the source codes that is missing from the list or any broken link, please kindly let me know.

Image denoising

Image coding

Image demosaicing

Image interpolation and Superresolution

RGBD image processing

Image segmentation/parsing and matting

Image deblurring

Blind image deblurring

Texture synthesis

Image inpainting

PDE-based image processing

Image quality assessment

Biometrics

Gradient-domain image processing

Video coding

Texture/Shape/Image classification

Object detection/recognition

Image registration/mosaicing/OFE

Action/event/scene recognition

Visual tracking

Saliency/Objectness Detection

 

Low rank methods

Deep Learning

Manifold learning and embedding

Wavelets and frames

Compressed Sensing

HDR imaging

Biomedical Imaging

2D Phase Unwrapping

DTI and fiber tractography

Data Clustering

Stereo matching& Multiview geometry

Graphics, Cartoons,Motion&3D

 

Machine learning&Neural Networks

Blind source separation

Camera calibration

Sampling&Physics-based Simulation

Evolutionary computing/Optimization

Networking Research

Miscellaneous

Links to other communities' reproducible research effort

Links to reproducible books/journals/tutorials

Links to other individual's reproducible research

您可能感兴趣的与本文相关的镜像

AutoGPT

AutoGPT

AI应用

AutoGPT于2023年3月30日由游戏公司Significant Gravitas Ltd.的创始人Toran Bruce Richards发布,AutoGPT是一个AI agent(智能体),也是开源的应用程序,结合了GPT-4和GPT-3.5技术,给定自然语言的目标,它将尝试通过将其分解成子任务,并在自动循环中使用互联网和其他工具来实现这一目标

<think>嗯,用户这次在寻找格子玻尔兹曼方法(LBM)的MATLAB实现代码。从历史对话看,我此前提供过算法资源索引,但用户这次明确要求MATLAB代码层面的具体实现。用户可能正在做计算流体力学相关研究或课程作业。考虑引用[1]中三个专业LBM资源:NDSU的示例代码库、欧盟NECLAB的开源项目"anb"、以及Pohl的J-Lattice-Boltzmann工具包。这些都是可信赖的来源。特别注意引用[2]里那段matlab距离计算函数,虽然它是腔流问题的辅助工具,但展示LBM边界处理的典型matlab写法:通过矩阵操作避免循环计算。这值得推荐给用户学习边界处理技巧。需要提醒用户注意几点:LBM代码对网格规模敏感,二维模型运行速度尚可但三维需要并行优化;边界条件设置(如引用[2]的壁面距离计算)直接影响结果精度;初始和稳态条件设置需要参考文献参数。准备组织推荐资源时将教学型代码库(如NDSU的9个示例)放在首位,用户容易上手;而anb和J-LB更适合需要完整框架的情况。最后主动提供MATLAB实现的指导可帮助用户减少代码调试时间。</think> 以下是关于格子玻尔兹曼方法(Lattice Boltzmann Method, LBM)的MATLAB代码实现资源及实现思路,结合引用内容整理: --- ### **推荐资源** 1. **NDSU示例代码库** [^1] 链接:http://www.physics.ndsu.nodak.edu/wagner/LB.html - 提供9个经典LBM案例的MATLAB/C代码,涵盖泊肃叶流、顶盖驱动流等。 - 代码结构清晰,适合初学者理解算法框架。 2. **开源项目 'anb'** [^1] 链接:http://www.it.neclab.eu/lba/ - 完整的LBM求解器,支持多松弛时间模型(MRT)。 - 含MATLAB接口,需编译后调用。 3. **J-Lattice-Boltzmann工具箱** [^1] 链接:http://thomas-pohl.info/work/lba.html - MATLAB版LBM库(v0.92),包含二维/三维流场模拟示例。 --- ### **基础LBM实现步骤(MATLAB伪代码)** ```matlab %% 参数设置 nx = 100; ny = 100; % 网格尺寸 tau = 0.6; % 松弛时间 rho0 = 1; % 初始密度 u0 = [0, 0]; % 初始速度 w = [4/9, 1/9, 1/9, 1/9, 1/36, ...]; % D2Q9模型权重 %% 初始化分布函数 f = zeros(ny, nx, 9); feq = zeros(ny, nx, 9); for i = 1:9 f(:,:,i) = w(i) * rho0; % 初始平衡分布 end %% 主循环 for t = 1:1000 % 1. 计算宏观量 rho = sum(f, 3); % 密度 ux = sum(f.*cx, 3) ./ rho; % x方向速度 uy = sum(f.*cy, 3) ./ rho; % y方向速度 % 2. 计算平衡分布函数 for i = 1:9 cu = 3*(cx(i)*ux + cy(i)*uy); feq(:,:,i) = w(i)*rho.*(1 + cu + 0.5*cu.^2 - 1.5*(ux.^2+uy.^2)); end % 3. 碰撞步骤 f = f - (1/tau)*(f - feq); % 4. 迁移步骤(传播到相邻网格) f = stream(f); % 自定义函数实现粒子迁移 % 5. 边界处理(如引用[2]的壁面距离法) f = apply_boundary(f); end ``` --- ### **关键实现技巧** 1. **边界条件** - 引用[2]中`compute_wall_distances`函数可计算壁面距离,用于反弹边界条件: ```matlab % 示例:顶盖驱动流上壁面速度条件 f(top_wall, [4,7,8]) = feq(top_wall, [2,5,6]) + 6*w([4,7,8])*rho.*u_wall.*cy([4,7,8]); ``` 2. **并行优化** - 利用MATLAB矩阵运算避免循环(如`.^`和`.*`代替幂和乘法)。 3. **可视化** - 用`quiver(ux,uy)`绘制速度场,`contourf(rho)`显示密度分布。 --- ### **学习建议** 1. 从**NDSU的示例代码**入手,逐步修改参数(如雷诺数$Re$); 2. 在**顶盖驱动流**案例中尝试边界条件优化(参考引用[2]); 3. 复杂模型参考**'anb'项目**的MRT实现[^1]。 > 附:[西弗吉利亚大学代码合集](引用[3][^3]) 和 [Reproducible Research资源](引用[4][^4]) 包含更多LBM扩展算法(如多相流、热传递)。 --- **相关问题** 1. LBM中多松弛时间模型(MRT)相比BGK模型有何优势? 2. 如何用LBM模拟多相流动(如气泡上升)? 3. LBM在GPU并行计算中的优化策略? 4. LBM边界条件(如Zou-He边界)的MATLAB实现细节?
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值