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原文在这里:
下面是前端时间搜集整理的一些和计算机视觉、模式识别的资源,拿出来与大家分享下。以后,我将把图像处理真正的作为我的兴趣来玩玩了,也许不把研究作为谋生的手段,会更好些。
标题 | 作者 | 主题 | 关键字 | 类别 | 来源 | 备注 |
nipsfast.ppt | Nando de Freitas | N-Body problems in learning | Fast N-Body Learning | Ppt | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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nipsfgtf.ppt | Fast Multipole Methods Fast Gaussian Transform | FM and FGT | ppt | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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Gray.pdf/ppt | Alex Gray | Statistical N-Body/Proximity Data Structures | N-Body and Data Structures | Ppt/pdf | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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dt-nips04.pdf/ppt | Dan Huttenlocher | Fast Distance Transforms | FDT | Ppt/pdf | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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High.pdf/ppt | Alexander Gray | Fast high-dimensional function integration | Fast integration | Ppt/pdf | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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Fast04.pdf/ppt | David Lowe | Fast high-dimensional feature indexing for object recognition | Feature indexing | Ppt/pdf | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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ihler-fast.pdf/ppt | Alexander lhler | Fast methods and non-parametric BP | Non-parametric BP | Ppt/pdf | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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fastview.pdf | Dustin Lang | Comparing fast methods | Overview fast methods | | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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nbody_methods.tar.gz |
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| code | http://www.cs.ubc.ca/~awll/nbody_methods.html |
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demo_rbpf_gauss.tar |
| Rao Blackwellised particle filtering for conditionally Gaussian Models | particle filtering for conditionally | code | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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demorbpfdbn.tar.gz |
| Rao Blackwellised Particle Filtering |
| code | http://www.cs.ubc.ca/~nando/software.html |
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upf_demos.tar.gz |
| Unscented Particle Filter | Particle Filter | code | http://www.cs.ubc.ca/~nando/nipsfast/schedule.html |
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BPF_1_3.zip |
| Boosted Particle Filter | Tracking | code | http://www.cs.ubc.ca/~okumak/research.html | 1 |
flyer_14_800.mpg |
| Source image | Database | Image | http://www.cs.ubc.ca/~okumak/research.html | 1 |
trans_flyer_14_800.mpg |
| image transformed | Database | Image | http://www.cs.ubc.ca/~okumak/research.html | 1 |
LBP.c/h | Topi Mäenpää | LBP operator | Texture | code | http://www.ee.oulu.fi/~topiolli/cpplibs/files/ |
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calibr_v30.zip |
| Camera Calibration | Computer vision | code | _toolbox_for_matlab | 2 |
| LEAR(Learning and Recognition in Vision | Common dataset | Human/car horse soccer human actions | dataset | http://lear.inrialpes.fr/data | 3 |
Lic.zip/highlight.zip | Robby T. Tan | Color Constancy Through Inverse Intensity Chromaticity Space | Highlight Removal from single image | code | http://www.commsp.ee.ic.ac.uk/~rtan/ |
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2008_oxford_fog.pdf | Robby T. Tan | Defog | Defog from single | | http://www.commsp.ee.ic.ac.uk/~rtan/ |
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08_cvpr.pdf | Robby T. Tan | Defog | Defog from single | | http://www.commsp.ee.ic.ac.uk/~rtan/ |
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Retinex_frankle_mccann |
| Retinex |
| Code | http://www.cs.sfu.ca/~colour/publications/IST-2000/ | Some |
Retinex_maccann99 |
| Retinex |
| code | http://www.cs.sfu.ca/~colour/publications/IST-2000/ | pictures |
Gamut.tar.bz2 |
| Retinex |
| code | http://kobus.ca/research/programs/colour_constancy/index.html |
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Video.avi/dehaze.m |
| dehazing | Raanan Fattal | code | http://www.cs.huji.ac.il/~raananf/projects/defog/index.html |
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MPTK-Windows-bin-0-5-6-beta.zip | Matching pursuit(MP) | Alogrithm | CNRS | Code | http://mptk.irisa.fr/downloads |
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generateDictionaries.txt | GenerateGabor | Alogrithm |
| code | http://www.scholarpedia.org/article/Matching_pursuit |
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Notes:
1. 视频和源码都是对应的文章的:
Kenji Okuma, Ali Taleghani, Nando De Freitas, Jim Little, David G. Lowe. Boosted Particle Filter: Multitarget Detection and Tracking. the European Conference on Computer Vision(ECCV), May 2004.
2. 该网站下面还有其他一些资源可以下载:
http://www.ee.oulu.fi/mvg/page/downloads
是个研究组织: , 除此之外,还有一些源码。
计算机视觉文献与代码资源
CVonline
李子青的大作:
Markov Random Field Modeling in Computer Vision
Handbook of Face Recognition (PDF)
张正友的有关参数鲁棒估计著作:
Parameter Estimation Techniques:A Tutorial with Application to Conic FittingAndrea Fusiello“计算机视觉中的几何”教程:Elements of Geometric Computer Vision
有关马尔可夫蒙特卡罗方法的资料:
An introduction to Markov chain Monte Carlo Markov Chain Monte Carlo for Computer Vision--- A tutorial at ICCV05
有关独立成分分析(Independent Component Analysis , ICA)的资料:
An ICA-Page
Fast ICA
The Kalman Filter (介绍卡尔曼滤波器的终极网页)
Cached k-d tree search for ICP algorithms
几个计算机视觉研究工具 Machine Vision Toolbox for Matlab
Matlab and Octave Function for Computer Vision and Image Processing
Bayes Net Toolbox for Matlab
OpenCV (Chinese)
Gandalf (A Computer Vision and Numerical Algorithm Labrary)
CMU Computer Vision Home Page
Machine Learning Resource Links
The Bayesian Filtering Library
Optical Flow Algorithm Evaluation (提供了一个动态贝叶斯网络框架,例如递归信息处理与分析、卡尔曼滤波、粒子滤波、序列蒙特卡罗方法等,C++写的)
MATLAB code for ICP algorithm
牛人主页:
朱松纯(Song-Chun Zhu)
David Lowe (SIFT) (很帅的一个老头哦 ^ ^)
Andrea Vedaldi (SIFT)
Pedro F. Felzenszwalb
Dougla Dlanman (Brown的一个研究生,在其主页上搜集了大量算法教程和源码)
Jianbo Shi (Ncuts 的始作俑者)
Active Vision Group (Oxford的一个机器视觉研究团队,特色是SLAM,监视,导航)
Juyang Weng(机器学习的专家,Autonomous Mental Development 是其特色)
测试图片或视频:
Middlebury College‘s Stereo Vision Data Set
Intelligent Vehicle:
IVSource
Robot Car
How to Build a Robot: The Computer Vision Part
计算机视觉应关注的资源 |
来自美国帝腾大学的链接。 Links to toolboxes (mostly MATLAB) for camera calibration. . Modeling and Rendering Architecture from Photographs. , Tutorial on 3D Modeling from Images,, ECCV 2000, Available here: (12.1MB pdf) NIPS 2004 Tutorial on Acquiring Detailed 3D Models From Images and Video, Available here: (37.6 MB, ppt) did his thesis work on visual servoing for robot applications and has authored a robotics toolkit and vision toolkit for MATLAB. local copy of thesis: (4.36 MB, pdf) robot toolkit: (568 KB, zip) vision toolkit: (1.08 MB, zip), School of Computer Science & Software Engineering, The University of Western Australia. Available locally as a zip archive (4.8 MB, updated 21 May 2005), among many other contributions, submitted a Structure and motion toolkit in Matlab to the MathSoft File Exhange. Local copy here: (2.4 MB, zip). |
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