2016-11-27 22:37:47 yzhang6_10 阅读数 2631
  • 基于深度学习的计算机视觉:原理与实践(上部)

    本课程适合具有一定深度学习基础,希望发展为深度学习之计算机视觉方向的算法工程师和研发人员的同学们。 基于深度学习的计算机视觉是目前人工智能最活跃的领域,应用非常广泛,如人脸识别和无人驾驶中的机器视觉等。该领域的发展日新月异,网络模型和算法层出不穷。如何快速入门并达到可以从事研发的高度对新手和中级水平的学生而言面临不少的挑战。精心准备的本课程希望帮助大家尽快掌握基于深度学习的计算机视觉的基本原理、核心算法和当前的领先技术,从而有望成为深度学习之计算机视觉方向的算法工程师和研发人员。 本课程系统全面地讲述基于深度学习的计算机视觉技术的原理并进行项目实践。课程涵盖计算机视觉的七大任务,包括图像分类、目标检测、图像分割(语义分割、实例分割、全景分割)、人脸识别、图像描述、图像检索、图像生成(利用生成对抗网络)。本课程注重原理和实践相结合,逐篇深入解读经典和前沿论文70余篇,图文并茂破译算法难点, 使用思维导图梳理技术要点。项目实践使用Keras框架(后端为Tensorflow),学员可快速上手。 通过本课程的学习,学员可把握基于深度学习的计算机视觉的技术发展脉络,掌握相关技术原理和算法,有助于开展该领域的研究与开发实战工作。另外,深度学习之计算机视觉方向的知识结构及学习建议请参见本人CSDN博客。 本课程提供课程资料的课件PPT(pdf格式)和项目实践代码,方便学员学习和复习。 本课程分为上下两部分,其中上部包含课程的前五章(课程介绍、深度学习基础、图像分类、目标检测、图像分割),下部包含课程的后四章(人脸识别、图像描述、图像检索、图像生成)。

    3475 人正在学习 去看看 白勇

计算机视觉领域较好论文汇总

  • Learning to Track at 100 FPS with Deep Regression Networks (2016)
  • Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks (AAAI 2016)
  • Online Multi-target Tracking using Recurrent Neural Networks (2016)
  • Multi-Target Tracking by Discrete-Continuous Energy Minimization (2016)
  • Learning Multi-Domain Convolutional Neural Networks for Visual Tracking (VOT2015 冠军)
  • Learning to Track: Online Multi-Object Tracking by Decision Making (ICCV 2015)
  • Hierarchical Convolutional Features for Visual Tracking (ICCV 2015)
  • Robust Visual Tracking via Convolutional Networks without Training (2015)
  • Transferring Rich Feature Hierarchies for Robust Visual Tracking (2015)
  • Understanding and Diagnosing Visual Tracking Systems (ICCV 2015)
  • RATM: Recurrent Attentive Tracking Model (2015)
  • Visual Tracking with Fully Convolutional Networks (ICCV 2015)
  • Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor (2015)
  • High-speed Tracking with Kernelized Correlation filters(2015 TPAMI)
  • Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals (VOT2015)
  • Adaptive Decontamination of the Training Set:A Unified Formulation for Discriminative Visual Tracking (2016)
  • Sequentially Training Convolutional Networks for Visual Tracking (2016)
  • Complementary Learners for Real-Time Tracking (2016)
  • Siamese Instance Search for Tracking (2016)
  • learning multi-domain convolution neural networks for visual tracking (2016)
  • Visual Tracking with Fully Convolutional Networks (2016)
  • Learning to Track at 100 FPS with Deep Regression Networks
  • Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
  • DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
  • RCNN Rich feature hierarchies for accurate object detection andsemantic segmentation
  • SPP-net Spatial Pyramid Pooling in Deep Convolutional Networks forVisual Recognition
  • Fast R-CNN Towards Real-Time Object Detection with Region Proposal Networks
  • Faster R-CNN Faster R-CNN Towards Real-Time ObjectDetection with Region Proposal Networks
  • DeepTrack:Learning Discriminative Feature Representations by Convolutional Neural Networks for visual Tracking (2014)
  • Matching Networks for One Shot Learning (2016)
  • Factorized Convolutional Neural Networks (2016)
  • Multi-scale Patch Aggregation(MPA)for Simultaneous Detection and Segmentation (CVPR2016)
  • DeepFashion:Powering Robust Clothes Recognition and Retrieval With Rich Annotations (2016)
  • A Key Volume Mining Deep Framework for Action Recognition (2016)
  • Joint Training of Cascaded CNN for Face Detection (2016)
  • Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2014)
  • Deep Learning Face Representation from Predicting 10,000 Classes (CVPR2014)
  • Recover Canonical-View Faces in the Wild with Deep Neural Network (CVPR2014)
  • Deep Neural Networks for Object Detection.
  • Is Faster R-CNN Doing Well for Pedestrain Detection?(ECCV 2016)
  • Ten Years of Pedestrian Detection, What Have We Learned? (ECCV 2014)
  • (未完待续)…
2020-02-21 12:02:47 qq_28306361 阅读数 34
  • 基于深度学习的计算机视觉:原理与实践(上部)

    本课程适合具有一定深度学习基础,希望发展为深度学习之计算机视觉方向的算法工程师和研发人员的同学们。 基于深度学习的计算机视觉是目前人工智能最活跃的领域,应用非常广泛,如人脸识别和无人驾驶中的机器视觉等。该领域的发展日新月异,网络模型和算法层出不穷。如何快速入门并达到可以从事研发的高度对新手和中级水平的学生而言面临不少的挑战。精心准备的本课程希望帮助大家尽快掌握基于深度学习的计算机视觉的基本原理、核心算法和当前的领先技术,从而有望成为深度学习之计算机视觉方向的算法工程师和研发人员。 本课程系统全面地讲述基于深度学习的计算机视觉技术的原理并进行项目实践。课程涵盖计算机视觉的七大任务,包括图像分类、目标检测、图像分割(语义分割、实例分割、全景分割)、人脸识别、图像描述、图像检索、图像生成(利用生成对抗网络)。本课程注重原理和实践相结合,逐篇深入解读经典和前沿论文70余篇,图文并茂破译算法难点, 使用思维导图梳理技术要点。项目实践使用Keras框架(后端为Tensorflow),学员可快速上手。 通过本课程的学习,学员可把握基于深度学习的计算机视觉的技术发展脉络,掌握相关技术原理和算法,有助于开展该领域的研究与开发实战工作。另外,深度学习之计算机视觉方向的知识结构及学习建议请参见本人CSDN博客。 本课程提供课程资料的课件PPT(pdf格式)和项目实践代码,方便学员学习和复习。 本课程分为上下两部分,其中上部包含课程的前五章(课程介绍、深度学习基础、图像分类、目标检测、图像分割),下部包含课程的后四章(人脸识别、图像描述、图像检索、图像生成)。

    3475 人正在学习 去看看 白勇
2017-09-27 20:59:07 u013270326 阅读数 285
  • 基于深度学习的计算机视觉:原理与实践(上部)

    本课程适合具有一定深度学习基础,希望发展为深度学习之计算机视觉方向的算法工程师和研发人员的同学们。 基于深度学习的计算机视觉是目前人工智能最活跃的领域,应用非常广泛,如人脸识别和无人驾驶中的机器视觉等。该领域的发展日新月异,网络模型和算法层出不穷。如何快速入门并达到可以从事研发的高度对新手和中级水平的学生而言面临不少的挑战。精心准备的本课程希望帮助大家尽快掌握基于深度学习的计算机视觉的基本原理、核心算法和当前的领先技术,从而有望成为深度学习之计算机视觉方向的算法工程师和研发人员。 本课程系统全面地讲述基于深度学习的计算机视觉技术的原理并进行项目实践。课程涵盖计算机视觉的七大任务,包括图像分类、目标检测、图像分割(语义分割、实例分割、全景分割)、人脸识别、图像描述、图像检索、图像生成(利用生成对抗网络)。本课程注重原理和实践相结合,逐篇深入解读经典和前沿论文70余篇,图文并茂破译算法难点, 使用思维导图梳理技术要点。项目实践使用Keras框架(后端为Tensorflow),学员可快速上手。 通过本课程的学习,学员可把握基于深度学习的计算机视觉的技术发展脉络,掌握相关技术原理和算法,有助于开展该领域的研究与开发实战工作。另外,深度学习之计算机视觉方向的知识结构及学习建议请参见本人CSDN博客。 本课程提供课程资料的课件PPT(pdf格式)和项目实践代码,方便学员学习和复习。 本课程分为上下两部分,其中上部包含课程的前五章(课程介绍、深度学习基础、图像分类、目标检测、图像分割),下部包含课程的后四章(人脸识别、图像描述、图像检索、图像生成)。

    3475 人正在学习 去看看 白勇

期刊会议论文下载:

http://cvpapers.com/

ICCV:IEEE International Conference on Computer Vision  国际计算机视觉大会

CVPR:IEEE Conference on Computer Vision and Pattern Recognition  国际计算机视觉与模式识别会议

ECCV:European Conference on Computer Vision 欧洲计算机视觉国际会议

ACCV:Asian Conference on Computer Vision 亚洲计算机视觉会议

SIGGRAPH:Special Interest Group for Computer Graphics and Interactive Techniques计算机图形和交互技术特别兴趣小组


http://www.springerlink.com/content/100272/  (IJCV的网址)

IJCV:International Journal of Computer Vision 计算机视觉国际期刊


http://books.nips.cc/ (NIPS官网,有论文下载列表)

NIPS:Conference and Workshop on Neural Information Processing Systems 神经信息处理系统大会


会议期刊相关信息:
http://conferences.visionbib.com/Iris-Conferences.html
该网页列出了图像处理,计算机视觉领域相关几乎所有比较出名的会议时间表。
http://conferences.visionbib.com/Browse-conf.php
上面网页的一个子网页,列出了最近的CV领域提交paper的deadline。

2019-07-16 11:31:25 qq_28250697 阅读数 192
  • 基于深度学习的计算机视觉:原理与实践(上部)

    本课程适合具有一定深度学习基础,希望发展为深度学习之计算机视觉方向的算法工程师和研发人员的同学们。 基于深度学习的计算机视觉是目前人工智能最活跃的领域,应用非常广泛,如人脸识别和无人驾驶中的机器视觉等。该领域的发展日新月异,网络模型和算法层出不穷。如何快速入门并达到可以从事研发的高度对新手和中级水平的学生而言面临不少的挑战。精心准备的本课程希望帮助大家尽快掌握基于深度学习的计算机视觉的基本原理、核心算法和当前的领先技术,从而有望成为深度学习之计算机视觉方向的算法工程师和研发人员。 本课程系统全面地讲述基于深度学习的计算机视觉技术的原理并进行项目实践。课程涵盖计算机视觉的七大任务,包括图像分类、目标检测、图像分割(语义分割、实例分割、全景分割)、人脸识别、图像描述、图像检索、图像生成(利用生成对抗网络)。本课程注重原理和实践相结合,逐篇深入解读经典和前沿论文70余篇,图文并茂破译算法难点, 使用思维导图梳理技术要点。项目实践使用Keras框架(后端为Tensorflow),学员可快速上手。 通过本课程的学习,学员可把握基于深度学习的计算机视觉的技术发展脉络,掌握相关技术原理和算法,有助于开展该领域的研究与开发实战工作。另外,深度学习之计算机视觉方向的知识结构及学习建议请参见本人CSDN博客。 本课程提供课程资料的课件PPT(pdf格式)和项目实践代码,方便学员学习和复习。 本课程分为上下两部分,其中上部包含课程的前五章(课程介绍、深度学习基础、图像分类、目标检测、图像分割),下部包含课程的后四章(人脸识别、图像描述、图像检索、图像生成)。

    3475 人正在学习 去看看 白勇

计算机视觉领域经典论文源码

在读一些大牛的论文后,总是想找些代码读一读,可是查找代码资源是如此的痛苦,经过一番请教和查找,将比较好的资源贴出来,方便大家使用,希望大家有什么更好的资源也能分享出来,可以贴在留言qu

 

2016-CVPR论文代码资源:

https://tensortalk.com/?cat=conference-cvpr-2016

 

 

 

一个GitHub账号,里面有很多计算机视觉领域最新论文的代码实现:

https://github.com/kjw0612/awesome-deep-vision

 


 

 

 

 

Type Topic Name Reference Link
Code Structure from motion libmv   http://code.google.com/p/libmv/
Code Dimension Reduction LLE   http://www.cs.nyu.edu/~roweis/lle/code.html
Code Clustering Spectral Clustering - UCSD Project   http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz
Code Clustering K-Means 323个Item- Oxford Code   http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip
Code Image Deblurring Non-blind deblurring (and blind denoising) with integrated noise estimation U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011 http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm
Code Structure from motion Structure from Motion toolbox for Matlab by Vincent Rabaud   http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/
Code Multiple View Geometry Matlab Functions for Multiple View Geometry   http://www.robots.ox.ac.uk/~vgg/hzbook/code/
Code Object Detection Max-Margin Hough Transform S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/
Code Image Segmentation SLIC Superpixels R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html
Code Visual Tracking Tracking using Pixel-Wise Posteriors C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008 http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml
Code Visual Tracking Visual Tracking with Histograms and Articulating Blocks S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008 http://www.cise.ufl.edu/~smshahed/tracking.htm
Code Sparse Representation Robust Sparse Coding for Face Recognition M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip
Code Feature Detection andFeature Extraction Groups of Adjacent Contour Segments V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz
Code Density Estimation Kernel Density Estimation Toolbox   http://www.ics.uci.edu/~ihler/code/kde.html
Code Illumination, Reflectance, and Shadow Ground shadow detection J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 http://www.jflalonde.org/software.html#shadowDetection
Code Image Denoising,Image Super-resolution, andImage Deblurring Learning Models of Natural Image Patches D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011 http://www.cs.huji.ac.il/~daniez/
Code Illumination, Reflectance, and Shadow Estimating Natural Illumination from a Single Outdoor Image J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel
Code Visual Tracking Lucas-Kanade affine template tracking S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002 http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking
Code Saliency Detection Saliency-based video segmentation K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009 http://www.brl.ntt.co.jp/people/akisato/saliency3.html
Code Dimension Reduction Laplacian Eigenmaps   http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar
Code Illumination, Reflectance, and Shadow What Does the Sky Tell Us About the Camera? J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel
Code Image Filtering SVM for Edge-Preserving Filtering Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010 http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip
Code Image Segmentation Recovering Occlusion Boundaries from a Single Image D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. http://www.cs.cmu.edu/~dhoiem/software/
Code Visual Tracking Visual Tracking Decomposition J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010 http://cv.snu.ac.kr/research/~vtd/
Code Visual Tracking GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007 http://cs.unc.edu/~ssinha/Research/GPU_KLT/
Code Object Detection Recognition using regions C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009 http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip
Code Saliency Detection Saliency Using Natural statistics L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008 http://cseweb.ucsd.edu/~l6zhang/
Code Image Filtering Local Laplacian Filters S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011 http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip
Code Common Visual Pattern Discovery Sketching the Common S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz
Code Image Denoising BLS-GSM   http://decsai.ugr.es/~javier/denoise/
Code Camera Calibration Epipolar Geometry Toolbox G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005 http://egt.dii.unisi.it/
Code Depth Sensor Kinect SDK http://www.microsoft.com/en-us/kinectforwindows/ http://www.microsoft.com/en-us/kinectforwindows/
Code Image Super-resolution Self-Similarities for Single Frame Super-Resolution C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 https://eng.ucmerced.edu/people/cyang35/ACCV10.zip
Code Image Denoising Gaussian Field of Experts   http://www.cs.huji.ac.il/~yweiss/BRFOE.zip
Code Object Detection Poselet L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 http://www.eecs.berkeley.edu/~lbourdev/poselets/
Code Kernels and Distances Efficient Earth Mover’s Distance with L1 Ground Distance (EMD_L1) H. Ling and K. Okada, An Efficient Earth Mover’s Distance Algorithm for Robust Histogram Comparison, PAMI 2007 http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip
Code Nearest Neighbors Matching Spectral Hashing Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 http://www.cs.huji.ac.il/~yweiss/SpectralHashing/
Code Image Denoising Field of Experts   http://www.cs.brown.edu/~roth/research/software.html
Code Image Segmentation Multiscale Segmentation Tree E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 andN. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 http://vision.ai.uiuc.edu/segmentation
Code Multiple Instance Learning MILIS Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010  
Code Nearest Neighbors Matching FLANN: Fast Library for Approximate Nearest Neighbors   http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
Code Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) - VLFeat J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.vlfeat.org/
Code Alpha Matting Spectral Matting A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008 http://www.vision.huji.ac.il/SpectralMatting/
Code Multi-View Stereo Patch-based Multi-view Stereo Software Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 http://grail.cs.washington.edu/software/pmvs/
Code Clustering Self-Tuning Spectral Clustering   http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html
Code Feature Extraction andObject Detection Histogram of Oriented Graidents - OLT for windows N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.computing.edu.au/~12482661/hog.html
Code Image Understanding Nonparametric Scene Parsing via Label Transfer C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 http://people.csail.mit.edu/celiu/LabelTransfer/index.html
Code Multiple Kernel Learning DOGMA F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 http://dogma.sourceforge.net/
Code Distance Metric Learning Matlab Toolkit for Distance Metric Learning   http://www.cs.cmu.edu/~liuy/distlearn.htm
Code Optical Flow Black and Anandan’s Optical Flow   http://www.cs.brown.edu/~dqsun/code/ba.zip
Code Text Recognition Text recognition in the wild K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011 http://vision.ucsd.edu/~kai/grocr/
Code MRF Optimization MRF Minimization Evaluation R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 http://vision.middlebury.edu/MRF/
Code Saliency Detection Context-aware saliency detection S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html
Code Saliency Detection Learning to Predict Where Humans Look T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009 http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html
Code Stereo Stereo Evaluation D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001 http://vision.middlebury.edu/stereo/
Code Image Segmentation Quick-Shift A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 http://www.vlfeat.org/overview/quickshift.html
Code Saliency Detection Graph-based visual saliency J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007 http://www.klab.caltech.edu/~harel/share/gbvs.php
Code Clustering K-Means - VLFeat   http://www.vlfeat.org/
Code Object Detection A simple object detector with boosting ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
Code Image Quality Assessment Structural SIMilarity   https://ece.uwaterloo.ca/~z70wang/research/ssim/
Code Structure from motion FIT3D   http://www.fit3d.info/
Code Image Denoising BM3D   http://www.cs.tut.fi/~foi/GCF-BM3D/
Code Saliency Detection Discriminant Saliency for Visual Recognition from Cluttered Scenes D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004 http://www.svcl.ucsd.edu/projects/saliency/
Code Image Denoising Nonlocal means with cluster trees T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008 http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip
Code Saliency Detection Global Contrast based Salient Region Detection M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011 http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/
Code Visual Tracking Motion Tracking in Image Sequences C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000 http://www.cs.berkeley.edu/~flw/tracker/
Code Saliency Detection Itti, Koch, and Niebur’ saliency detection L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998 http://www.saliencytoolbox.net/
Code Feature Detection,Feature Extraction, andAction Recognition Space-Time Interest Points (STIP) I. Laptev, On Space-Time Interest Points, IJCV, 2005 and I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005 http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zipandhttp://www.nada.kth.se/cvap/abstracts/cvap284.html
Code Texture Synthesis Image Quilting for Texture Synthesis and Transfer A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001 http://www.cs.cmu.edu/~efros/quilt_research_code.zip
Code Image Denoising Non-local Means   http://dmi.uib.es/~abuades/codis/NLmeansfilter.m
Code Low-Rank Modeling TILT: Transform Invariant Low-rank Textures Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 http://perception.csl.uiuc.edu/matrix-rank/tilt.html
Code Object Proposal Objectness measure B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz
Code Image Filtering Real-time O(1) Bilateral Filtering Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009 http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip
Code Image Quality Assessment SPIQA   http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip
Code Object Recognition Biologically motivated object recognition T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 http://cbcl.mit.edu/software-datasets/standardmodel/index.html
Code Illumination, Reflectance, and Shadow Shadow Detection using Paired Region R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 http://www.cs.illinois.edu/homes/guo29/projects/shadow.html
Code Illumination, Reflectance, and Shadow Real-time Specular Highlight Removal Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip
Code MRF Optimization Max-flow/min-cut Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 http://vision.csd.uwo.ca/code/maxflow-v3.01.zip
Code Optical Flow Optical Flow Evaluation S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 http://vision.middlebury.edu/flow/
Code Image Super-resolution MRF for image super-resolution W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html
Code MRF Optimization Planar Graph Cut F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip
Code Object Detection Feature Combination P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html
Code Structure from motion VisualSFM : A Visual Structure from Motion System   http://www.cs.washington.edu/homes/ccwu/vsfm/
Code Nearest Neighbors Matching ANN: Approximate Nearest Neighbor Searching   http://www.cs.umd.edu/~mount/ANN/
Code Saliency Detection Learning Hierarchical Image Representation with Sparsity, Saliency and Locality J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011  
Code Optical Flow Optical Flow by Deqing Sun D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 http://www.cs.brown.edu/~dqsun/code/flow_code.zip
Code Image Understanding Discriminative Models for Multi-Class Object Layout C. Desai, D. Ramanan, C. Fowlkes. “Discriminative Models for Multi-Class Object Layout, IJCV 2011 http://www.ics.uci.edu/~desaic/multiobject_context.zip
Code Graph Matching Hyper-graph Matching via Reweighted Random Walks J. Lee, M. Cho, K. M. Lee. “Hyper-graph Matching via Reweighted Random Walks”, CVPR 2011 http://cv.snu.ac.kr/research/~RRWHM/
Code Object Detection Hough Forests for Object Detection J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009 http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html
Code Object Discovery Using Multiple Segmentations to Discover Objects and their Extent in Image Collections B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006 http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html
Code Dimension Reduction Diffusion maps   http://www.stat.cmu.edu/~annlee/software.htm
Code Multiple Kernel Learning SHOGUN S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006 http://www.shogun-toolbox.org/
Code Distance Transformation Distance Transforms of Sampled Functions   http://people.cs.uchicago.edu/~pff/dt/
Code Image Filtering Image smoothing via L0 Gradient Minimization L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011 http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip
Code Feature Extraction PCA-SIFT Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004 http://www.cs.cmu.edu/~yke/pcasift/
Code Visual Tracking Particle Filter Object Tracking   http://blogs.oregonstate.edu/hess/code/particles/
Code Feature Extraction sRD-SIFT M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010 http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html#
Code Multiple Instance Learning MILES Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/
Code Action Recognition Dense Trajectories Video Description H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011 http://lear.inrialpes.fr/people/wang/dense_trajectories
Code Image Segmentation Efficient Graph-based Image Segmentation - C++ code P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://people.cs.uchicago.edu/~pff/segment/
Code Object Proposal Parametric min-cut J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 http://sminchisescu.ins.uni-bonn.de/code/cpmc/
Code Common Visual Pattern Discovery Common Visual Pattern Discovery via Spatially Coherent Correspondences H. Liu, S. Yan, “Common Visual Pattern Discovery via Spatially Coherent Correspondences”, CVPR 2010 https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0
Code Sparse Representation Sparse coding simulation software Olshausen BA, Field DJ, “Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images”, Nature 1996 http://redwood.berkeley.edu/bruno/sparsenet/
Code MRF Optimization Max-flow/min-cut for massive grids A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008 http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip
Code Optical Flow Horn and Schunck’s Optical Flow   http://www.cs.brown.edu/~dqsun/code/hs.zip
Code Sparse Representation Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar
Code Image Understanding Towards Total Scene Understanding L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 http://vision.stanford.edu/projects/totalscene/index.html
Code Camera Calibration Camera Calibration Toolbox for Matlab http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html http://www.vision.caltech.edu/bouguetj/calib_doc/
Code Image Segmentation Turbepixels A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 http://www.cs.toronto.edu/~babalex/research.html
Code Feature Detection Edge Foci Interest Points L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011 http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm
Code Feature Extraction Local Self-Similarity Descriptor E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007 http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/
Code Subspace Learning Generalized Principal Component Analysis R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003 http://www.vision.jhu.edu/downloads/main.php?dlID=c1
Code Camera Calibration EasyCamCalib J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009 http://arthronav.isr.uc.pt/easycamcalib/
Code Image Segmentation Superpixel by Gerg Mori X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 http://www.cs.sfu.ca/~mori/research/superpixels/
Code Image Understanding Object Bank Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010 http://vision.stanford.edu/projects/objectbank/index.html
Code Saliency Detection Spectrum Scale Space based Visual Saliency J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011 http://www.cim.mcgill.ca/~lijian/saliency.htm
Code Sparse Representation Fisher Discrimination Dictionary Learning for Sparse Representation M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip
Code Object Detection Cascade Object Detection with Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 http://people.cs.uchicago.edu/~rbg/star-cascade/
Code Object Segmentation Sparse to Dense Labeling P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz
Code Optical Flow Dense Point Tracking N. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010 http://lmb.informatik.uni-freiburg.de/resources/binaries/
Code Visual Tracking Tracking with Online Multiple Instance Learning B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011 http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
Code Graph Matching Reweighted Random Walks for Graph Matching M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010 http://cv.snu.ac.kr/research/~RRWM/
Code Machine Learning Statistical Pattern Recognition Toolbox M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 http://cmp.felk.cvut.cz/cmp/software/stprtool/
Code Image Super-resolution Sprarse coding super-resolution J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 http://www.ifp.illinois.edu/~jyang29/ScSR.htm
Code Object Detection Discriminatively Trained Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 http://people.cs.uchicago.edu/~pff/latent/
Code Multiple Instance Learning MIForests C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 http://www.ymer.org/amir/software/milforests/
Code Optical Flow Large Displacement Optical Flow T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/
Code Multiple View Geometry MATLAB and Octave Functions for Computer Vision and Image Processing P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html
Code Image Filtering Anisotropic Diffusion P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990 http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik
Code Feature Detection andFeature Extraction Geometric Blur A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005 http://www.robots.ox.ac.uk/~vgg/software/MKL/
Code Low-Rank Modeling Low-Rank Matrix Recovery and Completion   http://perception.csl.uiuc.edu/matrix-rank/sample_code.html
Code Object Detection A simple parts and structure object detector ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html
Code Kernels and Distances Diffusion-based distance H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 http://www.dabi.temple.edu/~hbling/code/DD_v1.zip
Code Image Denoising K-SVD   http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip
Code Multiple Kernel Learning SimpleMKL A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008 http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html
Code Feature Extraction Pyramids of Histograms of Oriented Gradients (PHOG) A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007 http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip
Code Sparse Representation Efficient sparse coding algorithms H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007 http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm
Code Multi-View Stereo Clustering Views for Multi-view Stereo Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 http://grail.cs.washington.edu/software/cmvs/
Code Multi-View Stereo Multi-View Stereo Evaluation S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 http://vision.middlebury.edu/mview/
Code Structure from motion Structure and Motion Toolkit in Matlab   http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm
Code Pose Estimation Training Deformable Models for Localization Ramanan, D. “Learning to Parse Images of Articulated Bodies.” NIPS 2006 http://www.ics.uci.edu/~dramanan/papers/parse/index.html
Code Low-Rank Modeling RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010 http://perception.csl.uiuc.edu/matrix-rank/rasl.html
Code Dimension Reduction ISOMAP   http://isomap.stanford.edu/
Code Alpha Matting Learning-based Matting Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 http://www.mathworks.com/matlabcentral/fileexchange/31412
Code Image Segmentation Normalized Cut J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 http://www.cis.upenn.edu/~jshi/software/
Code Image Denoising andStereo Matching Efficient Belief Propagation for Early Vision P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006 http://www.cs.brown.edu/~pff/bp/
Code Sparse Representation A Linear Subspace Learning Approach via Sparse Coding L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip
Code Text Recognition Neocognitron for handwritten digit recognition K. Fukushima: “Neocognitron for handwritten digit recognition”, Neurocomputing, 2003 http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375
Code Image Classification Sparse Coding for Image Classification J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 http://www.ifp.illinois.edu/~jyang29/ScSPM.htm
Code Nearest Neighbors Matching LDAHash: Binary Descriptors for Matching in Large Image Databases C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011. http://cvlab.epfl.ch/research/detect/ldahash/index.php
Code Object Segmentation ClassCut for Unsupervised Class Segmentation B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010 http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip
Code Image Quality Assessment Feature SIMilarity Index   http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm
Code Saliency Detection Attention via Information Maximization N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005 http://www.cse.yorku.ca/~neil/AIM.zip
Code Image Denoising What makes a good model of natural images ? Y. Weiss and W. T. Freeman, CVPR 2007 http://www.cs.huji.ac.il/~yweiss/BRFOE.zip
Code Image Segmentation Mean-Shift Image Segmentation - Matlab Wrapper D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz
Code Object Segmentation Geodesic Star Convexity for Interactive Image Segmentation V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml
Code Feature Detection andFeature Extraction Affine-SIFT J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009 http://www.ipol.im/pub/algo/my_affine_sift/
Code MRF Optimization Multi-label optimization Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 http://vision.csd.uwo.ca/code/gco-v3.0.zip
Code Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - Demo Software D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://www.cs.ubc.ca/~lowe/keypoints/
Code Visual Tracking KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981 http://www.ces.clemson.edu/~stb/klt/
Code Feature Detection andFeature Extraction Affine Covariant Features T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008 http://www.robots.ox.ac.uk/~vgg/research/affine/
Code Image Segmentation Segmenting Scenes by Matching Image Composites B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009 http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html
Code Image Segmentation OWT-UCM Hierarchical Segmentation P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
Code Feature Matching andImage Classification The Pyramid Match: Efficient Matching for Retrieval and Recognition K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005 http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm
Code Alpha Matting Bayesian Matting Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001 http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html
Code Image Deblurring Richardson-Lucy Deblurring for Scenes under Projective Motion Path Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011 http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip
Code Pose Estimation Articulated Pose Estimation using Flexible Mixtures of Parts Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011 http://phoenix.ics.uci.edu/software/pose/
Code Feature Extraction BRIEF: Binary Robust Independent Elementary Features M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010 http://cvlab.epfl.ch/research/detect/brief/
Code Feature Extraction Global and Efficient Self-Similarity T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010andT. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz
Code Image Super-resolution Multi-frame image super-resolution Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis http://www.robots.ox.ac.uk/~vgg/software/SR/index.html
Code Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - Library D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://blogs.oregonstate.edu/hess/code/sift/
Code Image Denoising Clustering-based Denoising P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009 http://users.soe.ucsc.edu/~priyam/K-LLD/
Code Object Recognition Recognition by Association via Learning Per-exemplar Distances T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008 http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz
Code Visual Tracking Superpixel Tracking S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011 http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html
Code Sparse Representation SPArse Modeling Software J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010 http://www.di.ens.fr/willow/SPAMS/
Code Saliency Detection Saliency detection: A spectral residual approach X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007 http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html
Code Image Filtering Guided Image Filtering K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010 http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar
Code Kernels and Distances Fast Directional Chamfer Matching   http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip
Code Visual Tracking L1 Tracking X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009 http://www.dabi.temple.edu/~hbling/code_data.htm
Code Object Proposal Region-based Object Proposal I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010 http://vision.cs.uiuc.edu/proposals/
Code Object Detection Ensemble of Exemplar-SVMs for Object Detection and Beyond T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011 http://www.cs.cmu.edu/~tmalisie/projects/iccv11/
Code Dimension Reduction Dimensionality Reduction Toolbox   http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
Code Object Detection Viola-Jones Object Detection P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001 http://pr.willowgarage.com/wiki/FaceDetection
Code Object Detection Implicit Shape Model B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008 http://www.vision.ee.ethz.ch/~bleibe/code/ism.html
Code Saliency Detection Saliency detection using maximum symmetric surround R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010 http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html
Code Image Filtering Fast Bilateral Filter S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006 http://people.csail.mit.edu/sparis/bf/
Code Machine Learning FastICA package for MATLAB http://research.ics.tkk.fi/ica/book/ http://research.ics.tkk.fi/ica/fastica/
Code Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.robots.ox.ac.uk/~vgg/research/affine/
Code Structure from motion Bundler N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006 http://phototour.cs.washington.edu/bundler/
Code Visual Tracking Online Discriminative Object Tracking with Local Sparse Representation Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012 http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip
Code Alpha Matting Closed Form Matting A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008. http://people.csail.mit.edu/alevin/matting.tar.gz
Code Image Filtering GradientShop P. Bhat, C.L. Zitnick, M. Cohen, B. Curless, and J. Kim, GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering, TOG 2010 http://grail.cs.washington.edu/projects/gradientshop/
Code Visual Tracking Incremental Learning for Robust Visual Tracking D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 http://www.cs.toronto.edu/~dross/ivt/
Code Feature Detection andFeature Extraction Color Descriptor K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010 http://koen.me/research/colordescriptors/
Code Image Segmentation Entropy Rate Superpixel Segmentation M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip
Code Image Filtering Domain Transformation E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011 http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip
Code Multiple Kernel Learning OpenKernel.org F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011 http://www.openkernel.org/
Code Image Segmentation Efficient Graph-based Image Segmentation - Matlab Wrapper P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation
Code Image Segmentation Biased Normalized Cut S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/
Code Stereo Constant-Space Belief Propagation Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm
Code Feature Detection andFeature Extraction Speeded Up Robust Feature (SURF) - Open SURF H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 http://www.chrisevansdev.com/computer-vision-opensurf.html
Code Visual Tracking Online boosting trackers H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006 http://www.vision.ee.ethz.ch/boostingTrackers/
Code Image Denoising Sparsity-based Image Denoising W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011 http://www.csee.wvu.edu/~xinl/CSR.html
Code Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - VLFeat D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://www.vlfeat.org/
Code Clustering Spectral Clustering - UW Project   http://www.stat.washington.edu/spectral/
Code Image Deblurring Analyzing spatially varying blur A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010 http://www.eecs.harvard.edu/~ayanc/svblur/
Code Multiple Instance Learning DD-SVM Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004  
Code Feature Extraction GIST Descriptor A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001 http://people.csail.mit.edu/torralba/code/spatialenvelope/
Code Image Classification Texture Classification M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005 http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html
Code Structure from motion Nonrigid Structure From Motion in Trajectory Space   http://cvlab.lums.edu.pk/nrsfm/index.html
Code Alpha Matting Shared Matting E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010 http://www.inf.ufrgs.br/~eslgastal/SharedMatting/
Code Action Recognition 3D Gradients (HOG3D) A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008. http://lear.inrialpes.fr/people/klaeser/research_hog3d
Code Image Denoising Kernel Regressions   http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip
Code Feature Detection Boundary Preserving Dense Local Regions J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011 http://vision.cs.utexas.edu/projects/bplr/bplr.html
Code Image Understanding SuperParsing J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010 http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip
Code Image Filtering Weighted Least Squares Filter Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008 http://www.cs.huji.ac.il/~danix/epd/
Code Image Super-resolution Single-Image Super-Resolution Matlab Package R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010 http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip
Code Image Understanding Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010 http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads
Code Feature Extraction Shape Context S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html
Code Image Processing andImage Filtering Piotr’s Image & Video Matlab Toolbox Piotr Dollar, Piotr’s Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
Code Illumination, Reflectance, and Shadow Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel
Code Pose Estimation Calvin Upper-Body Detector E. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009 http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/
Code Image Classification Locality-constrained Linear Coding J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 http://www.ifp.illinois.edu/~jyang29/LLC.htm
Code Feature Detection andFeature Extraction Speeded Up Robust Feature (SURF) - Matlab Wrapper H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php
Code Pose Estimation Estimating Human Pose from Occluded Images J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009 http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip
Code Structure from motion OpenSourcePhotogrammetry   http://opensourcephotogrammetry.blogspot.com/
Code Image Classification Spatial Pyramid Matching S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip
Code Nearest Neighbors Matching Coherency Sensitive Hashing S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011 http://www.eng.tau.ac.il/~simonk/CSH/index.html
Code Image Segmentation Segmentation by Minimum Code Length A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007 http://perception.csl.uiuc.edu/coding/image_segmentation/
Code Saliency Detection Frequency-tuned salient region detection R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009 http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html
Code MRF Optimization Max-flow/min-cut for shape fitting V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007 http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip
Code Feature Detection Canny Edge Detection J. Canny, A Computational Approach To Edge Detection, PAMI, 1986 http://www.mathworks.com/help/toolbox/images/ref/edge.html
Code Object Detection Multiple Kernels A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009 http://www.robots.ox.ac.uk/~vgg/software/MKL/
Code Image Segmentation Mean-Shift Image Segmentation - EDISON D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 http://coewww.rutgers.edu/riul/research/code/EDISON/index.html
Code Image Quality Assessment Degradation Model   http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html
Code Object Detection Ensemble of Exemplar-SVMs T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011 http://www.cs.cmu.edu/~tmalisie/projects/iccv11/
Code Image Deblurring Radon Transform T. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011 http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip
Code Image Deblurring Eficient Marginal Likelihood Optimization in Blind Deconvolution A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011 http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip
Code Feature Detection FAST Corner Detection E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006 http://www.edwardrosten.com/work/fast.html
Code Image Super-resolution MDSP Resolution Enhancement Software S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004 http://users.soe.ucsc.edu/~milanfar/software/superresolution.html
Code Feature Extraction andObject Detection Histogram of Oriented Graidents - INRIA Object Localization Toolkit N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.navneetdalal.com/software
Code Visual Tracking Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects H. Pirsiavash, D. Ramanan, C. Fowlkes. “Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011 http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz
Code Saliency Detection Segmenting salient objects from images and videos E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010 http://www.cse.oulu.fi/MVG/Downloads/saliency
Code Visual Tracking Object Tracking A. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006 http://plaza.ufl.edu/lvtaoran/object%20tracking.htm
Code Machine Learning Boosting Resources by Liangliang Cao http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm
Code Machine Learning Netlab Neural Network Software C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995 http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/
Code Optical Flow Classical Variational Optical Flow T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 http://lmb.informatik.uni-freiburg.de/resources/binaries/
Code Sparse Representation Centralized Sparse Representation for Image Restoration W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip
Course Computer Vision Introduction to Computer Vision, Stanford University, Winter 2010-2011 Fei-Fei Li http://vision.stanford.edu/teaching/cs223b/
Course Computer Vision Computer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012 Silvio Savarese and Fei-Fei Li https://www.coursera.org/course/computervision
Course Computer Vision Computer Vision, University of Texas at Austin, Spring 2011 Kristen Grauman http://www.cs.utexas.edu/~grauman/courses/spring2011/index.html
Course Computer Vision Learning-Based Methods in Vision, CMU, Spring 2012 Alexei “Alyosha” Efros and Leonid Sigal https://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0
Course Visual Recognition Visual Recognition, University of Texas at Austin, Fall 2011 Kristen Grauman http://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.html
Course Computer Vision Introduction to Computer Vision James Hays, Brown University, Fall 2011 http://www.cs.brown.edu/courses/cs143/
Course Computer Vision Computer Vision, University of North Carolina at Chapel Hill, Spring 2010 Svetlana Lazebnik http://www.cs.unc.edu/~lazebnik/spring10/
Course Computer Vision Computer Vision: The Fundamentals, University of California at Berkeley, Fall 2012 Jitendra Malik https://www.coursera.org/course/vision
Course Computational Photography Computational Photography, University of Illinois, Urbana-Champaign, Fall 2011 Derek Hoiem http://www.cs.illinois.edu/class/fa11/cs498dh/
Course Graphical Models Inference in Graphical Models, Stanford University, Spring 2012 Andrea Montanari, Stanford University http://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.html
Course Computer Vision Computer Vision, New York University, Fall 2012 Rob Fergus http://cs.nyu.edu/~fergus/teaching/vision_2012/index.html
Course Computer Vision Advances in Computer Vision Antonio Torralba, MIT, Spring 2010 http://groups.csail.mit.edu/vision/courses/6.869/
Course Computer Vision Computer Vision, University of Illinois, Urbana-Champaign, Spring 2012 Derek Hoiem http://www.cs.illinois.edu/class/sp12/cs543/
Course Computational Photography Computational Photography, CMU, Fall 2011 Alexei “Alyosha” Efros http://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.html
Course Computer Vision Computer Vision, University of Washington, Winter 2012 Steven Seitz http://www.cs.washington.edu/education/courses/cse455/12wi/
Link Source code Source Code Collection for Reproducible Research collected by Xin Li, Lane Dept of CSEE, West Virginia University http://www.csee.wvu.edu/~xinl/reproducible_research.html
Link Computer Vision Computer Image Analysis, Computer Vision Conferences USC http://iris.usc.edu/information/Iris-Conferences.html
Link Computer Vision CV Papers on the web CVPapers http://www.cvpapers.com/index.html
Link Computer Vision CVonline CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision http://homepages.inf.ed.ac.uk/rbf/CVonline/
Link Dataset Compiled list of recognition datasets compiled by Kristen Grauman http://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htm
Link Computer Vision Annotated Computer Vision Bibliography compiled by Keith Price http://iris.usc.edu/Vision-Notes/bibliography/contents.html
Link Computer Vision The Computer Vision homepage   http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
Link Computer Vision Industry The Computer Vision Industry David Lowe http://www.cs.ubc.ca/~lowe/vision.html
Link Source code Computer Vision Algorithm Implementations CVPapers http://www.cvpapers.com/rr.html
Link Computer Vision CV Datasets on the web CVPapers http://www.cvpapers.com/datasets.html
Talk Visual Recognition Understanding Visual Scenes Antonio Torralba, MIT http://videolectures.net/nips09_torralba_uvs/
Talk Neuroscience Learning in Hierarchical Architectures: from Neuroscience to Derived Kernels Tomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technology http://videolectures.net/mlss09us_poggio_lhandk/
Talk Deep Learning A tutorial on Deep Learning Geoffrey E. Hinton, Department of Computer Science, University of Toronto http://videolectures.net/jul09_hinton_deeplearn/
Talk Boosting Theory and Applications of Boosting Robert Schapire, Department of Computer Science, Princeton University http://videolectures.net/mlss09us_schapire_tab/
Talk Graphical Models Graphical Models and message-passing algorithms Martin J. Wainwright, University of California at Berkeley http://videolectures.net/mlss2011_wainwright_messagepassing/
Talk Statistical Learning Theory Statistical Learning Theory John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London http://videolectures.net/mlss04_taylor_slt/
Talk Gaussian Process Gaussian Process Basics David MacKay, University of Cambridge http://videolectures.net/gpip06_mackay_gpb/
Talk Information Theory Information Theory David MacKay, University of Cambridge http://videolectures.net/mlss09uk_mackay_it/
Talk Optimization Optimization Algorithms in Machine Learning Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison http://videolectures.net/nips2010_wright_oaml/
Talk Bayesian Inference Introduction To Bayesian Inference Christopher Bishop, Microsoft Research http://videolectures.net/mlss09uk_bishop_ibi/
Talk Bayesian Nonparametrics Modern Bayesian Nonparametrics Peter Orbanz and Yee Whye Teh http://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfu
Talk Kernels and Distances Machine learning and kernel methods for computer vision Francis R. Bach, INRIA http://videolectures.net/etvc08_bach_mlakm/
Talk Optimization Convex Optimization Lieven Vandenberghe, Electrical Engineering Department, University of California, Los Angeles http://videolectures.net/mlss2011_vandenberghe_convex/
Talk Optimization Energy Minimization with Label costs and Applications in Multi-Model Fitting Yuri Boykov, Department of Computer Science, University of Western Ontario http://videolectures.net/nipsworkshops2010_boykov_eml/
Talk Object Detection Object Recognition with Deformable Models Pedro Felzenszwalb, Brown University http://www.youtube.com/watch?v=_J_clwqQ4gI
Talk Low-level vision Learning and Inference in Low-Level Vision Yair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalem http://videolectures.net/nips09_weiss_lil/
Talk 3D Computer Vision 3D Computer Vision: Past, Present, and Future Steven Seitz, University of Washington, Google Tech Talk, 2011 http://www.youtube.com/watch?v=kyIzMr917Rc
Talk Optimization Who is Afraid of Non-Convex Loss Functions? Yann LeCun, New York University http://videolectures.net/eml07_lecun_wia/
Talk Sparse Representation Sparse Methods for Machine Learning: Theory and Algorithms Francis R. Bach, INRIA http://videolectures.net/nips09_bach_smm/
Talk Optimization and Support Vector Machines Optimization Algorithms in Support Vector Machines Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison http://videolectures.net/mlss09us_wright_oasvm/
Talk Information Theory Information Theory in Learning and Control Naftali (Tali) Tishby, The Hebrew University http://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfu
Talk Relative Entropy Relative Entropy Sergio Verdu, Princeton University http://videolectures.net/nips09_verdu_re/
Tutorial Object Detection Geometry constrained parts based detection Simon Lucey, Jason Saragih, ICCV 2011 Tutorial http://ci2cv.net/tutorials/iccv-2011/
Tutorial Graphical Models Learning with inference for discrete graphical models Nikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorial http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/
Tutorial Variational Calculus Variational methods for computer vision Daniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorial http://cvpr.in.tum.de/tutorials/iccv2011
Tutorial 3D perception Computer Vision and 3D Perception for Robotics Radu Bogdan Rusu, Gary Bradski, Caroline Pantofaru, Stefan Hinterstoisser, Stefan Holzer, Kurt Konolige and Andrea Vedaldi, ECCV 2010 Tutorial http://www.willowgarage.com/workshops/2010/eccv
Tutorial Action Recognition Looking at people: The past, the present and the future L. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorial http://www.cs.brown.edu/~ls/iccv2011tutorial.html
Tutorial Non-linear Least Squares Computer vision fundamentals: robust non-linear least-squares and their applications Pascal Fua, Vincent Lepetit, ICCV 2011 Tutorial http://cvlab.epfl.ch/~fua/courses/lsq/
Tutorial Action Recognition Frontiers of Human Activity Analysis J. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorial http://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/
Tutorial Structured Prediction Structured Prediction and Learning in Computer Vision S. Nowozin and C. Lampert, CVPR 2011 Tutorial http://www.nowozin.net/sebastian/cvpr2011tutorial/
Tutorial Action Recognition Statistical and Structural Recognition of Human Actions Ivan Laptev and Greg Mori, ECCV 2010 Tutorial https://sites.google.com/site/humanactionstutorialeccv10/
Tutorial Computational Symmetry Computational Symmetry: Past, Current, Future Yanxi Liu, ECCV 2010 Tutorial http://vision.cse.psu.edu/research/symmComp/index.shtml
Tutorial Matlab Matlab Tutorial David Kriegman and Serge Belongie http://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.html
Tutorial Matlab Writing Fast MATLAB Code Pascal Getreuer, Yale University http://www.mathworks.com/matlabcentral/fileexchange/5685
Tutorial Spectral Clustering A Tutorial on Spectral Clustering Ulrike von Luxburg, Max Planck Institute for Biological Cybernetics http://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdf
Tutorial Feature Learning, Image Classification Feature Learning for Image Classification Kai Yu and Andrew Ng, ECCV 2010 Tutorial http://ufldl.stanford.edu/eccv10-tutorial/
Tutorial Shape Analysis, Diffusion Geometry Diffusion Geometry Methods in Shape Analysis A. Brontein and M. Bronstein, ECCV 2010 Tutorial http://tosca.cs.technion.ac.il/book/course_eccv10.html
Tutorial Graphical Models Graphical Models, Exponential Families, and Variational Inference Martin J. Wainwright and Michael I. Jordan, University of California at Berkeley http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf
Tutorial Color Image Processing Color image understanding: from acquisition to high-level image understanding Theo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorial http://www.cat.uab.cat/~joost/tutorial_iccv.html
Tutorial Structure from motion Nonrigid Structure from Motion Y. Sheikh and Sohaib Khan, ECCV 2010 Tutorial http://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.html
Tutorial Expectation Maximization A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models Jeff A. Bilmes, University of California at Berkeley http://crow.ee.washington.edu/people/bulyko/papers/em.pdf
Tutorial Decision Forests Decision forests for classification, regression, clustering and density estimation A. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorial http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx
Tutorial 3D point cloud processing 3D point cloud processing: PCL (Point Cloud Library) R. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorial http://www.pointclouds.org/media/iccv2011.html
Tutorial Image Registration Tools and Methods for Image Registration Brown, G. Carneiro, A. A. Farag, E. Hancock, A. A. Goshtasby (Organizer), J. Matas, J.M. Morel, N. S. Netanyahu, F. Sur, and G. Yu, CVPR 2011 Tutorial http://www.imgfsr.com/CVPR2011/Tutorial6/
Tutorial Non-rigid registration Non-rigid registration and reconstruction Alessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorial http://www.isr.ist.utl.pt/~adb/tutorial/
Tutorial Variational Calculus Variational Methods in Computer Vision D. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorial http://cvpr.cs.tum.edu/tutorials/eccv2010
Tutorial Distance Metric Learning Distance Functions and Metric Learning M. Werman, O. Pele and B. Kulis, ECCV 2010 Tutorial http://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/
Tutorial Feature Extraction Image and Video Description with Local Binary Pattern Variants M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial http://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdf
Tutorial Game Theory Game Theory in Computer Vision and Pattern Recognition Marcello Pelillo and Andrea Torsello, CVPR 2011 Tutorial http://www.dsi.unive.it/~atorsell/cvpr2011tutorial/
Tutorial Computational Imaging Fcam: an architecture and API for computational cameras Kari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorial http://fcam.garage.maemo.org/iccv2011.html

 

 

 

 

Conference Information

 

 

 




 

另一博客整理的资源:

一、特征提取Feature Extraction:

 

二、图像分割Image Segmentation:

  • Normalized Cut [1] [Matlab code]
  • Gerg Mori’ Superpixel code [2] [Matlab code]
  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
  • Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
  • OWT-UCM Hierarchical Segmentation [5] [Resources]
  • Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
  • Quick-Shift [7] [VLFeat]
  • SLIC Superpixels [8] [Project]
  • Segmentation by Minimum Code Length [9] [Project]
  • Biased Normalized Cut [10] [Project]
  • Segmentation Tree [11-12] [Project]
  • Entropy Rate Superpixel Segmentation [13] [Code]
  • Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
  • Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
  • Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
  • Random Walks for Image Segmentation[Paper][Code]
  • Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
  • An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
  • Geodesic Star Convexity for Interactive Image Segmentation[Project]
  • Contour Detection and Image Segmentation Resources[Project][Code]
  • Biased Normalized Cuts[Project]
  • Max-flow/min-cut[Project]
  • Chan-Vese Segmentation using Level Set[Project]
  • A Toolbox of Level Set Methods[Project]
  • Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
  • Improved C-V active contour model[Paper][Code]
  • A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
  • Level Set Method Research by Chunming Li[Project]
  • ClassCut for Unsupervised Class Segmentation[code]
  • SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]

 

三、目标检测Object Detection:

  • A simple object detector with boosting [Project]
  • INRIA Object Detection and Localization Toolkit [1] [Project]
  • Discriminatively Trained Deformable Part Models [2] [Project]
  • Cascade Object Detection with Deformable Part Models [3] [Project]
  • Poselet [4] [Project]
  • Implicit Shape Model [5] [Project]
  • Viola and Jones’s Face Detection [6] [Project]
  • Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]
  • Hand detection using multiple proposals[Project]
  • Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]
  • Discriminatively trained deformable part models[Project]
  • Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]
  • Image Processing On Line[Project]
  • Robust Optical Flow Estimation[Project]
  • Where’s Waldo: Matching People in Images of Crowds[Project]
  • Scalable Multi-class Object Detection[Project]
  • Class-Specific Hough Forests for Object Detection[Project]
  • Deformed Lattice Detection In Real-World Images[Project]
  • Discriminatively trained deformable part models[Project]

 

四、显著性检测Saliency Detection:

  • Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]
  • Frequency-tuned salient region detection [2] [Project]
  • Saliency detection using maximum symmetric surround [3] [Project]
  • Attention via Information Maximization [4] [Matlab code]
  • Context-aware saliency detection [5] [Matlab code]
  • Graph-based visual saliency [6] [Matlab code]
  • Saliency detection: A spectral residual approach. [7] [Matlab code]
  • Segmenting salient objects from images and videos. [8] [Matlab code]
  • Saliency Using Natural statistics. [9] [Matlab code]
  • Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]
  • Learning to Predict Where Humans Look [11] [Project]
  • Global Contrast based Salient Region Detection [12] [Project]
  • Bayesian Saliency via Low and Mid Level Cues[Project]
  • Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]
  • Saliency Detection: A Spectral Residual Approach[Code]

 

五、图像分类、聚类Image Classification, Clustering

  • Pyramid Match [1] [Project]
  • Spatial Pyramid Matching [2] [Code]
  • Locality-constrained Linear Coding [3] [Project] [Matlab code]
  • Sparse Coding [4] [Project] [Matlab code]
  • Texture Classification [5] [Project]
  • Multiple Kernels for Image Classification [6] [Project]
  • Feature Combination [7] [Project]
  • SuperParsing [Code]
  • Large Scale Correlation Clustering Optimization[Matlab code]
  • Detecting and Sketching the Common[Project]
  • Self-Tuning Spectral Clustering[Project][Code]
  • User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]
  • Filters for Texture Classification[Project]
  • Multiple Kernel Learning for Image Classification[Project]
  • SLIC Superpixels[Project]

 

六、抠图Image Matting

  • A Closed Form Solution to Natural Image Matting [Code]
  • Spectral Matting [Project]
  • Learning-based Matting [Code]

 

七、目标跟踪Object Tracking:

  • A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]
  • Object Tracking via Partial Least Squares Analysis[Paper][Code]
  • Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]
  • Online Visual Tracking with Histograms and Articulating Blocks[Project]
  • Incremental Learning for Robust Visual Tracking[Project]
  • Real-time Compressive Tracking[Project]
  • Robust Object Tracking via Sparsity-based Collaborative Model[Project]
  • Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]
  • Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]
  • Superpixel Tracking[Project]
  • Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]
  • Online Multiple Support Instance Tracking [Paper][Code]
  • Visual Tracking with Online Multiple Instance Learning[Project]
  • Object detection and recognition[Project]
  • Compressive Sensing Resources[Project]
  • Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]
  • Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
  • the HandVu:vision-based hand gesture interface[Project]
  • Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]

 

八、Kinect:

 

九、3D相关:

  • 3D Reconstruction of a Moving Object[Paper] [Code]
  • Shape From Shading Using Linear Approximation[Code]
  • Combining Shape from Shading and Stereo Depth Maps[Project][Code]
  • Shape from Shading: A Survey[Paper][Code]
  • A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
  • Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
  • A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
  • Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
  • Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
  • Learning 3-D Scene Structure from a Single Still Image[Project]

 

十、机器学习算法

  • Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]
  • Random Sampling[code]
  • Probabilistic Latent Semantic Analysis (pLSA)[Code]
  • FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
  • Fast Intersection / Additive Kernel SVMs[Project]
  • SVM[Code]
  • Ensemble learning[Project]
  • Deep Learning[Net]
  • Deep Learning Methods for Vision[Project]
  • Neural Network for Recognition of Handwritten Digits[Project]
  • Training a deep autoencoder or a classifier on MNIST digits[Project]
  • THE MNIST DATABASE of handwritten digits[Project]
  • Ersatz:deep neural networks in the cloud[Project]
  • Deep Learning [Project]
  • sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
  • Weka 3: Data Mining Software in Java[Project]
  • Invited talk “A Tutorial on Deep Learning” by Dr. Kai Yu (余凯)[Video]
  • CNN - Convolutional neural network class[Matlab Tool]
  • Yann LeCun’s Publications[Wedsite]
  • LeNet-5, convolutional neural networks[Project]
  • Training a deep autoencoder or a classifier on MNIST digits[Project]
  • Deep Learning 大牛Geoffrey E. Hinton’s HomePage[Website]
  • Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]
  • Sparse coding simulation software[Project]
  • Visual Recognition and Machine Learning Summer School[Software]

 

十一、目标、行为识别Object, Action Recognition:

  • Action Recognition by Dense Trajectories[Project][Code]
  • Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
  • Recognition Using Regions[Paper][Code]
  • 2D Articulated Human Pose Estimation[Project]
  • Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
  • Estimating Human Pose from Occluded Images[Paper][Code]
  • Quasi-dense wide baseline matching[Project]
  • ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]
  • Real Time Head Pose Estimation with Random Regression Forests[Project]
  • 2D Action Recognition Serves 3D Human Pose Estimation[Project]
  • A Hough Transform-Based Voting Framework for Action Recognition[Project]
  • Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]
  • 2D articulated human pose estimation software[Project]
  • Learning and detecting shape models [code]
  • Progressive Search Space Reduction for Human Pose Estimation[Project]
  • Learning Non-Rigid 3D Shape from 2D Motion[Project]

 

十二、图像处理:

  • Distance Transforms of Sampled Functions[Project]
  • The Computer Vision Homepage[Project]
  • Efficient appearance distances between windows[code]
  • Image Exploration algorithm[code]
  • Motion Magnification 运动放大 [Project]
  • Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]
  • A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

 

十三、一些实用工具:

  • EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
  • a development kit of matlab mex functions for OpenCV library[Project]
  • Fast Artificial Neural Network Library[Project]

 

十四、人手及指尖检测与识别:

  • finger-detection-and-gesture-recognition [Code]
  • Hand and Finger Detection using JavaCV[Project]
  • Hand and fingers detection[Code]

 

十五、场景解释:

  • Nonparametric Scene Parsing via Label Transfer [Project]

 

十六、光流Optical flow:

  • High accuracy optical flow using a theory for warping [Project]
  • Dense Trajectories Video Description [Project]
  • SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]
  • KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]
  • Tracking Cars Using Optical Flow[Project]
  • Secrets of optical flow estimation and their principles[Project]
  • implmentation of the Black and Anandan dense optical flow method[Project]
  • Optical Flow Computation[Project]
  • Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]
  • A Database and Evaluation Methodology for Optical Flow[Project]
  • optical flow relative[Project]
  • Robust Optical Flow Estimation [Project]
  • optical flow[Project]

 

十七、图像检索Image Retrieval:

  • Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]

 

十八、马尔科夫随机场Markov Random Fields:

  • Markov Random Fields for Super-Resolution [Project]
  • A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]

 

十九、运动检测Motion detection:

  • Moving Object Extraction, Using Models or Analysis of Regions [Project]
  • Background Subtraction: Experiments and Improvements for ViBe [Project]
  • A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]
  • changedetection.net: A new change detection benchmark dataset[Project]
  • ViBe - a powerful technique for background detection and subtraction in video sequences[Project]
  • Background Subtraction Program[Project]
  • Motion Detection Algorithms[Project]
  • Stuttgart Artificial Background Subtraction Dataset[Project]
  • Object Detection, Motion Estimation, and Tracking[Project]

 

Feature Detection and Description

General Libraries: 

  • VLFeat – Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors (including DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. See Modern features: Software – Slides providing a demonstration of VLFeat and also links to other software. Check also VLFeat hands-on session training
  • OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

 

Fast Keypoint Detectors for Real-time Applications: 

  • FAST – High-speed corner detector implementation for a wide variety of platforms
  • AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV 2010).

 

Binary Descriptors for Real-Time Applications: 

  • BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)
  • ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)
  • BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)
  • FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)

 

SIFT and SURF Implementations: 

 

Other Local Feature Detectors and Descriptors: 

  • VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.
  • LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).
  • Local Symmetry Features – Source code for matching of local symmetry features under large variations in lighting, age, and rendering style (CVPR 2012).

 

Global Image Descriptors: 

  • GIST – Matlab code for the GIST descriptor
  • CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)

 

Feature Coding and Pooling 

  • VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding.
  • Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)

 

Convolutional Nets and Deep Learning 

  • EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.
  • Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.
  • Deep Learning - Various links for deep learning software.

 

Part-Based Models 

 

Attributes and Semantic Features 

 

Large-Scale Learning 

  • Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
  • LIBLINEAR – Library for large-scale linear SVM classification.
  • VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.

 

Fast Indexing and Image Retrieval 

  • FLANN – Library for performing fast approximate nearest neighbor.
  • Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
  • ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).
  • INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

 

Object Detection 

 

3D Recognition 

 

Action Recognition 


 

Datasets

 

Attributes 

  • Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
  • aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
  • FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
  • PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
  • LFW – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
  • Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.
  • SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
  • ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
  • Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.
  • Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.

 

Fine-grained Visual Categorization 

 

Face Detection 

  • FDDB – UMass face detection dataset and benchmark (5,000+ faces)
  • CMU/MIT – Classical face detection dataset.

 

Face Recognition 

  • Face Recognition Homepage – Large collection of face recognition datasets.
  • LFW – UMass unconstrained face recognition dataset (13,000+ face images).
  • NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
  • CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
  • FERET – Classical face recognition dataset.
  • Deng Cai’s face dataset in Matlab Format – Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.
  • SCFace – Low-resolution face dataset captured from surveillance cameras.

 

Handwritten Digits 

  • MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.

 

Pedestrian Detection

 

Generic Object Recognition 

  • ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
  • Tiny Images – 80 million 32x32 low resolution images.
  • Pascal VOC – One of the most influential visual recognition datasets.
  • Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.
  • MIT LabelMe – Online annotation tool for building computer vision databases.

 

Scene Recognition

 

Feature Detection and Description 

 

Action Recognition

 

RGBD Recognition 

 

下面是另一博客整理的资源:

 

cvchina搞到的机器视觉开源处理库汇总,转来了,很给力,还在不断更新。。。

通用库/General Library

无需多言。

Recognition And Vision Library. 线程安全。强大的IO机制。包含AAM。

很酷的一个图像处理包。整个库只有一个头文件。包含一个基于PDE的光流算法。

图像,视频IO/Image, Video IO

AR相关/Augmented Reality

基于Marker的AR库

ARToolKit的增强版。实现了更好的姿态估计算法。

实时的跟踪、SLAM、AR库。无需Marker,模板,内置传感器等。

基于特征点检测和识别的AR库。

局部不变特征/Local Invariant Feature

目前最好的Sift开源实现。同时包含了KD-tree,KD-Forest,BoW实现。

基于Naive Bayesian Bundle的特征点识别。高速,但占用内存高。

基于OpenCV的Sift实现。

目标检测/Object Detection

又一个AdaBoost实现。训练速度快。

基于Centrist和Linear SVM的快速行人检测。

(近似)最近邻/ANN

目前最完整的(近似)最近邻开源库。不但实现了一系列查找算法,还包含了一种自动选取最快算法的机制。

另外一个近似最近邻库。

SLAM & SFM

monoSLAM库。由Androw Davison开发。

图像分割/Segmentation

使用Simple Linear Iterative Clustering产生指定数目,近似均匀分布的Super Pixel。

目标跟踪/Tracking

基于Online Random Forest的目标跟踪算法。

Kanade-Lucas-Tracker

Online Boosting Trackers

直线检测/Line Detection

基于联通域连接的直线检测算法。

基于梯度的,局部直线段检测算子。

指纹/Finger Print

基于感知的多媒体文件Hash算法。(提取,对比图像、视频、音频的指纹)

视觉显著性/Visual Salience

Ming-Ming Cheng的视觉显著性算法。

FFT/DWT

最快,最好的开源FFT。

轻量级的FFT实现。许可证是亮点。

音频处理/Audio processing

音频处理,音频合成。

音频文件IO。

音频重采样。

小波变换

快速小波变换(FWT)

BRIEF: Binary Robust Independent Elementary Feature 一个很好的局部特征描述子,里面有FAST corner + BRIEF实现特征点匹配的DEMO:http://cvlab.epfl.ch/software/brief/

http://code.google.com/p/javacv


Java打包的opencv, FFmpeg, libdc1394, PGR FlyCapture, OpenKinect, videoInput, and ARToolKitPlus库。可以放在Android上用~

 

libHIK,HIK SVM,计算HIK SVM跟Centrist的Lib。http://c2inet.sce.ntu.edu.sg/Jianxin/projects/libHIK/libHIK.htm

 

一组视觉显著性检测代码的链接:http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/

 

 

 

介绍n款计算机视觉库/人脸识别开源库/软件

 

计算机视觉库 OpenCV

OpenCV是Intel®开源计算机视觉库。它由一系列 C 函数和少量 C++ 类构成,实现了图像处理和计算机视觉方面的很多通用算法。 OpenCV 拥有包括 300 多个C函数的跨平台的中、高层 API。它不依赖于其它的外部库——尽管也可以使用某些外部库。 OpenCV 对非商业…

人脸识别 faceservice.cgi

faceservice.cgi 是一个用来进行人脸识别的 CGI 程序, 你可以通过上传图像,然后该程序即告诉你人脸的大概座标位置。faceservice是采用 OpenCV 库进行开发的。

OpenCV的.NET版 OpenCVDotNet

OpenCVDotNet 是一个 .NET 对 OpenCV 包的封装。

人脸检测算法 jViolajones

jViolajones是人脸检测算法Viola-Jones的一个Java实现,并能够加载OpenCV XML文件。 示例代码:http://www.oschina.net/code/snippet_12_2033

Java视觉处理库 JavaCV

JavaCV 提供了在计算机视觉领域的封装库,包括:OpenCV、ARToolKitPlus、libdc1394 2.x 、PGR FlyCapture和FFmpeg。此外,该工具可以很容易地使用Java平台的功能。 JavaCV还带有硬件加速的全屏幕图像显示(CanvasFrame),易于在多个内核中执行并行代码(并…

运动检测程序 QMotion

QMotion 是一个采用 OpenCV 开发的运动检测程序,基于 QT。

视频监控系统 OpenVSS

OpenVSS - 开放平台的视频监控系统 - 是一个系统级别的视频监控软件视频分析框架(VAF)的视频分析与检索和播放服务,记录和索引技术。它被设计成插件式的支持多摄像头平台,多分析仪模块(OpenCV的集成),以及多核心架构

手势识别 hand-gesture-detection

手势识别,用OpenCV实现

人脸检测识别 mcvai-tracking

提供人脸检测、识别与检测特定人脸的功能,示例代码 cvReleaseImage( &gray ); cvReleaseMemStorage(&storage); cvReleaseHaarClassifierCascade(&cascade);…

人脸检测与跟踪库 asmlibrary

Active Shape Model Library (ASMLibrary©) SDK, 用OpenCV开发,用于人脸检测与跟踪。

Lua视觉开发库 libecv

ECV 是 lua 的计算机视觉开发库(目前只提供Linux支持)

OpenCV的.Net封装 OpenCVSharp

OpenCVSharp 是一个OpenCV的.Net wrapper,应用最新的OpenCV库开发,使用习惯比EmguCV更接近原始的OpenCV,有详细的使用样例供参考。

3D视觉库 fvision2010

基于OpenCV构建的图像处理和3D视觉库。 示例代码: ImageSequenceReaderFactory factory; ImageSequenceReader* reader = factory.pathRegex(“c:/a/im_%03d.jpg”, 0, 20); //ImageSequenceReader* reader = factory.avi(“a.avi”); if (reader == NULL) { …

基于QT的计算机视觉库 QVision

基于 QT 的面向对象的多平台计算机视觉库。可以方便的创建图形化应用程序,算法库主要从 OpenCV,GSL,CGAL,IPP,Octave 等高性能库借鉴而来。

图像特征提取 cvBlob

cvBlob 是计算机视觉应用中在二值图像里寻找连通域的库.能够执行连通域分析与特征提取.

实时图像/视频处理滤波开发包 GShow

GShow is a real-time image/video processing filter development kit. It successfully integrates DirectX11 with DirectShow framework. So it has the following features: GShow 是实时 图像/视频 处理滤波开发包,集成DiretX11。…

视频捕获 API VideoMan

VideoMan 提供一组视频捕获 API 。支持多种视频流同时输入(视频传输线、USB摄像头和视频文件等)。能利用 OpenGL 对输入进行处理,方便的与 OpenCV,CUDA 等集成开发计算机视觉系统。

开放模式识别项目 OpenPR

Pattern Recognition project(开放模式识别项目),致力于开发出一套包含图像处理、计算机视觉、自然语言处理、模式识别、机器学习和相关领域算法的函数库。

OpenCV的Python封装 pyopencv

OpenCV的Python封装,主要特性包括: 提供与OpenCV 2.x中最新的C++接口极为相似的python接口,并且包括C++中不包括的C接口 提供对OpenCV 2.x中所有主要部件的绑定:CxCORE (almost complete), CxFLANN (complete), Cv (complete), CvAux (C++ part almost…

视觉快速开发平台 qcv

计算机视觉快速开发平台,提供测试框架,使开发者可以专注于算法研究。

图像捕获 libv4l2cam

对函数库v412的封装,从网络摄像头等硬件获得图像数据,支持YUYV裸数据输出和BGR24的OpenCV  IplImage输出

计算机视觉算法 OpenVIDIA

OpenVIDIA projects implement computer vision algorithms running on on graphics hardware such as single or multiple graphics processing units(GPUs) using OpenGL, Cg and CUDA-C. Some samples will soon support OpenCL and Direct Compute API'…

高斯模型点集配准算法 gmmreg

实现了基于混合高斯模型的点集配准算法,该算法描述在论文: A Robust Algorithm for Point Set Registration Using Mixture of Gaussians, Bing Jian and Baba C. Vemuri. ,实现了C++/Matlab/Python接口…

模式识别和视觉库 RAVL

Recognition And Vision Library (RAVL) 是一个通用 C++ 库,包含计算机视觉、模式识别等模块。

图像处理和计算机视觉常用算法库 LTI-Lib

LTI-Lib 是一个包含图像处理和计算机视觉常用算法和数据结构的面向对象库,提供 Windows 下的 VC 版本和 linux 下的 gcc 版本,主要包含以下几方面内容: 1、线性代数 2、聚类分析 3、图像处理 4、可视化和绘图工具

OpenCV优化 opencv-dsp-acceleration

优化了OpenCV库在DSP上的速度。

C++计算机视觉库 Integrating Vision Toolkit

Integrating Vision Toolkit (IVT) 是一个强大而迅速的C++计算机视觉库,拥有易用的接口和面向对象的架构,并且含有自己的一套跨平台GUI组件,另外可以选择集成OpenCV

计算机视觉和机器人技术的工具包 EGT

The Epipolar Geometry Toolbox (EGT) is a toolbox designed for Matlab (by Mathworks Inc.). EGT provides a wide set of functions to approach computer vision and robotics problems with single and multiple views, and with different vision se…

OpenCV的扩展库 ImageNets

ImageNets 是对OpenCV 的扩展,提供对机器人视觉算法方面友好的支持,使用Nokia的QT编写界面。

libvideogfx

视频处理、计算机视觉和计算机图形学的快速开发库。

Matlab计算机视觉包 mVision

Matlab 的计算机视觉包,包含用于观察结果的 GUI 组件,貌似也停止开发了,拿来做学习用挺不错的。

Scilab的计算机视觉库 SIP

SIP 是 Scilab(一种免费的类Matlab编程环境)的图像处理和计算机视觉库。SIP 可以读写 JPEG/PNG/BMP 格式的图片。具备图像滤波、分割、边缘检测、形态学处理和形状分析等功能。

STAIR Vision Library

STAIR Vision Library (SVL) 最初是为支持斯坦福智能机器人设计的,提供对计算机视觉、机器学习和概率统计模

 

几种图像处理类库的比较

 

作者:王先荣

原文;http://www.cnblogs.com/xrwang/archive/2010/01/26/TheComparisonOfImageProcessingLibraries.html

前言

近期需要做一些图像处理方面的学习和研究,首要任务就是选择一套合适的图像处理类库。目前较知名且功能完善的图像处理类库有OpenCv、EmguCv、AForge.NET等等。本文将从许可协议、下载、安装、文档资料、易用性、性能等方面对这些类库进行比较,然后给出选择建议,当然也包括我自己的选择。

 

许可协议

类库 许可协议 许可协议网址 大致介绍
OpenCv BSD www.opensource.org/licenses/bsd-license.html 在保留原来BSD协议声明的前提下,随便怎么用都行
EmguCv GPL v3 http://www.gnu.org/licenses/gpl-3.0.txt 你的产品必须也使用GPL协议,开源且免费
商业授权 http://www.emgu.com/wiki/files/CommercialLicense.txt 给钱之后可以用于闭源的商业产品
AForge.net LGPL v3 http://www.gnu.org/licenses/lgpl.html 如果不修改类库源代码,引用该类库的产品可以闭源和(或)收费

以上三种类库都可以用于开发商业产品,但是EmguCv需要付费;因为我只是用来学习和研究,所以这些许可协议对我无所谓。不过鉴于我们身在中国,如果脸皮厚点,去他丫的许可协议。

 

下载

可以很方便的下载到这些类库,下载地址分别为:

类库

下载地址

OpenCv

http://sourceforge.net/projects/opencvlibrary/files/

EmguCv

http://www.emgu.com/wiki/index.PHP/Download_And_Installation

AForge.Net

http://www.aforgenet.com/framework/downloads.html

 

安装

这些类库的安装都比较简单,直接运行安装程序,并点“下一步”即可完成。但是OpenCv在安装完之后还需要一些额外的处理才能在VS2008里面使用,在http://www.opencv.org.cn有一篇名为《VC2008 Express下安装OpenCv 2.0》的文章专门介绍了如何安装OpenCv。

类库

安装难易度

备注

OpenCv

比较容易

VC下使用需要重新编译

EmguCv

容易

 

AForge.net

容易

 

相信看这篇文章的人都不会被安装困扰。

 

文档资料 

类库

总体评价

书籍

网站

文档

示例

社区

备注

OpenCv

中等

中英文

中英文

中英文

较多

中文论坛

有中文资料但不完整

EmguCv

英文

英文

英文论坛

论坛人气很差

AForge.net

英文

英文

英文论坛

论坛人气很差

OpenCv有一些中文资料,另外两种的资料全是英文的;不过EmguCv建立在OpenCv的基础上,大部分OpenCv的资料可以用于EmguCv;而AForge.net是原生的.net类库,对GDI+有很多扩展,一些MSDN的资料可以借鉴。如果在查词典的基础上还看不懂英文文档,基本上可以放弃使用这些类库了。

 

易用性

易用性这玩意,主观意志和个人能力对它影响很大,下面是我的看法:

类库

易用性

备注

OpenCv

比较差

OpenCv大多数功能都以C风格函数形式提供,少部分功能以C++类提供。注意:2.0版将更多的功能封装成类了。

EmguCv

比较好

将OpenCv的绝大部分功能都包装成了.net类、结构或者枚举。不过文档不全,还是得对照OpenCv的文档去看才行。

AForge.net

纯.net类库,用起来很方便。

最近几年一直用的是C# ,把C和C++忘记得差不多了,况且本来C/C++我就不太熟,所以对OpenCv的看法恐怕有偏见。

 

 

视觉相关网站

 

 

 

这段时间因为项目的需要,我一直在折腾计算机视觉,尤其是双目立体视觉,代码、论文、工具箱等……占用了我几乎90%的工作时间,还在一点点地摸索,但进度实在不敢恭维,稍后我会把情况作个总结。

 

今天的主要任务就是和大家分享一些鄙人收藏的认为相当研究价值的网页:

 

Oxford大牛:Andrew Zisserman,http://www.robots.ox.ac.uk/~vgg/hzbook/code/,此人主要研究多幅图像的几何学,该网站提供了部分工具,相当实用,还有例子

 

西澳大利亚大学的Peter Kovesi:http://www.csse.uwa.edu.au/~pk/research/matlabfns/,提供了一些基本的matlab工具,主要内容涉及Computer Vision, Image Processing

 

CMU:http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html,该网站是我的最爱,尤其后面这个地址http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/v-groups.html,在这里提供了世界各地机构、大学在Computer Vision所涉及各领域的研究情况,包括Image Processing, Machine Vision,我后来也是通过它连接到了很多国外的网站

 

Cambridge:http://mi.eng.cam.ac.uk/milab.html,这是剑桥大学的机器智能实验室,里面有三个小组,Computer Vision & Robotics, Machine Intelligence, Speech,目前为止,Computer Vision & Robotics的一些研究成果对我日后的帮助可能会比较大,所以在此提及

 

大量计算机视觉方面的原版电子书:http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm,我今天先下了本Zisserman的书,呵呵,国外的原版书,虽然都是比较老的,但是对于基础的理解学习还是很有帮助的,至于目前的研究现状只能通过论文或者一些研究小组的网站

 

stanford:http://ai.stanford.edu/~asaxena/reconstruction3d/,这个网站是Andrew N.G老师和一个印度阿三的博士一起维护的,主要对於单张照片的三维重建,尤其他有个网页make3d.stanford.edu可以让你自己上传你的照片,通过网站来重建三维模型,这个网站对于刚开始接触Computer Vision的我来说,如获至宝,但有个致命问题就是make3d已经无法注册,我也多次给Andrew和印度阿三email,至今未回,郁闷,要是有这个网站的帐号,那还是相当爽的,不知道是不是由于他们的邮箱把我的email当成垃圾邮件过滤,哎,但这个stanford网站的贡献主要是代码,有很多computer vision的基础工具,貌似40M左右,全都是基于matlab的

 

caltech:http://www.vision.caltech.edu/bouguetj/calib_doc/,这是我们Computer Vision老师课件上的连接,主要是用于摄像机标定的工具集,当然也有涉及对标定图像三维重建的前期处理过程

 

JP Tarel:http://perso.lcpc.fr/tarel.jean-philippe/,这是他的个人主页,也是目前为止我发的email中,唯一一个给我回信的老外,因为我需要重建练习的正是他的图片集,我读过他的论文,但没有涉及代码的内容,再加上又是94年以前的论文,很多相关的引文,我都无法下载,在我的再三追问下,Tarel教授只告诉我,你可以按照我的那篇论文对足球进行重建,可是…你知道吗,你有很多图像处理的引文都下不了了,我只知道你通过那篇文章做了图像的预处理,根本不知道具体过程,当然我有幸找到过一篇90左右的论文,讲的是region-based segmentation,可是这文章里所有引文又是找不到的….悲剧的人生

 

开源软件网站:www.sourceforge.net

 

最后就是我们工大的Computer Vision大牛:sychen.com,我们Computer Vision课的老师,谦虚、低调,很有学者风范

 

总结:目前为止,我的个人感觉就是国外学者的论文包括刊登的资料大部分都是对原理进行的说明,并不是很在意具体的代码实现的讲解,而我却过分的关注于代码的实现,忽视Computer Vision的原理,国外学者对与自己相关领域的研究现状了解相当充分,对自己的工作进度更新也很勤快,很多好的网站我并没有完全列出来,在这里只是提了主要的几个,在这方面,我们国内的研究氛围有所不及,当然我选择的一些网站可能更多的是个人小组的研究介绍,不像一些专门从事领域研究的机构,会有那么多的权威资料,国外的网站有个很好的地方,就是有很多的免费资源,免费的matlab或者openCV工具集,免费的论文下载,课件下载等等,在这方面国内对于研究资源的共享,做得又有所差距,同样,国外的研究工具很多样,主要是matlab,一些发布的demo都使用C++写的,不过今天看到一个西班牙的研究机构(university of las palmas)用了个XMW的软件平台来实现图片的三维重建,data用的是人脸,而且国外的很多源代码基本上是在linux平台下完成的,对于我来说又是不方便,哎,可能要考虑装VM Ware了,不然双系统太累…..

 

目前,Computer Vision是全世界范围内自动化、计算机、数学领域的研究热点,综合性高,应用于医疗、军事、民用等等领域,其中有突出成绩的还是一下几所学校(个人见解):Cambridge(UK), Oxford(UK), CMU(US),Stanford(US),MIT(US),U.C.Berkeley(US),而UK的两所老牌高校,他们的实际应用领域丝毫不逊于stanford和CMU….

 

世界就是这样,当你不断的接触,不断的扩展你所能够及的边际就会发现自己越来越无知,还有很多很多不知道,发现还有很多自己都想不到但却已经实现的东西…..

 

革命远未成功,同志仍须努力,在CV的道路上前进…….

 

 

三篇整理了很多特征、机器学习算法、计算机视觉和模式识别领域经典论文等源码资源的

博客:

1.http://www.cnblogs.com/ajian005/archive/2012/11/04/2841171.html

2.http://blog.csdn.net/yf0811240333/article/details/42076677

3.http://www.cnblogs.com/einyboy/p/3594432.html

 

如果大家有什么好的资源,希望大家能在留言上共享一下,方便大家学习~~

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