UIUC同学Jia-Bin Huang收集的计算机视觉代码合集

目录(?)[-]

1. Topic
2. Resources
3. References
4. Feature Extraction
5. Image Segmentation
6. 
   
7. 
   
8. Object Detection
9. Saliency Detection
10. Image Classification
11. Category-Independent Object Proposal
12. MRF
13. Shadow Detection
14. Optical Flow
15. Object Tracking
16. Image Matting
17. Bilateral Filtering
18. Image Denoising
19. Image Super-Resolution
20. Image Deblurring
21. Image Quality Assessment
22. Density Estimation
23. Dimension Reduction
24. Sparse Coding
25. Low-Rank Matrix Completion
26. Nearest Neighbors matching
27. Steoreo
28. Structure from motion
29. Distance Transformation
30. Chamfer Matching
31. Clustering
32. Classification
33. Regression
34. Multiple Kernel Learning MKL
35. Multiple Instance Learning MIL
36. Other Utilities
37. Useful Links dataset lectures and other softwares

文地址：UIUC同学Jia-BinHuang收集的计算机视觉代码合集作者：千里8848UIUC的Jia-Bin Huang同学收集了很多计算机视觉方面的代码，链接如下：

https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

这些代码很实用，可以让我们站在巨人的肩膀上~~

Topic

Resources

References

Feature Extraction

• SIFT [1] [Demo program][SIFT Library] [VLFeat]

• PCA-SIFT [2] [Project]

• Affine-SIFT [3] [Project]

• SURF [4] [OpenSURF] [Matlab Wrapper]

• Affine Covariant Features [5] [Oxford project]

• MSER [6] [Oxford project] [VLFeat]

• Geometric Blur [7] [Code]

• Local Self-Similarity Descriptor [8] [Oxford implementation]

• Global and Efficient Self-Similarity [9] [Code]

• Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]

• GIST [11] [Project]

• Shape Context [12] [Project]

• Color Descriptor [13] [Project]

• Pyramids of Histograms of Oriented Gradients [Code]

• Space-Time Interest Points (STIP) [14] [Code]

• Boundary Preserving Dense Local Regions [15][Project]

1. D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. [PDF]
2. Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004. [PDF]
3. J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison.SIAM Journal on Imaging Sciences, 2009. [PDF]
4. H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features,ECCV, 2006. [PDF]
5. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool,A comparison of affine region detectors.IJCV, 2005. [PDF]
6. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions.BMVC, 2002. [PDF]
7. A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences.CVPR, 2005. [PDF]
8. E. Shechtman and M. Irani. Matching local self-similarities across images and videos,CVPR, 2007. [PDF]
9. T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection.CVPR2010. [PDF]
10. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection.CVPR2005. [PDF]
11. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope,IJCV, 2001. [PDF]
12. S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition usingshape contexts,PAMI, 2002. [PDF]
13. K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition,PAMI, 2010.
14. I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF]
15. J. Kim and K. Grauman, Boundary Preserving Dense Local Regions,CVPR2011. [PDF]

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]

1. J. Shi and J Malik, Normalized Cuts and Image Segmentation,PAMI, 2000 [PDF]
   
2. X. Ren and J. Malik. Learning a classification model for segmentation.ICCV, 2003. [PDF]
3. P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation,IJCV2004. [PDF]
4. D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis.PAMI2002. [PDF]
5. P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation.PAMI, 2011. [PDF]
6. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi,TurboPixels:Fast Superpixels Using Geometric Flows,PAMI 2009. [PDF]
7. A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking,ECCV, 2008. [PDF]
8. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels,EPFL Technical Report, 2010. [PDF]
   
9. A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression,CVIU, 2007. [PDF]
10. S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut,CVPR2011
11. E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,”  ACCV 2009. [PDF]
12. N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,”PAMI1996 [PDF]
13. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation,CVPR2011 [PDF]

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]

1. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection.CVPR2005. [PDF]
2. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
   Object Detection with Discriminatively Trained Part Based Models,PAMI, 2010 [PDF]
3. P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models.CVPR2010 [PDF]
4. L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations,ICCV2009 [PDF]
5. B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation,IJCV, 2008. [PDF]
6. P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features,CVPR2001. [PDF]

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]

1. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis.PAMI, 1998. [PDF]
2. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. InCVPR, 2009. [PDF]
3. R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. InICIP, 2010. [PDF]
4. N. Bruce and J. Tsotsos. Saliency based on information maximization. InNIPS, 2005. [PDF]
5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. InCVPR, 2010. [PDF]
6. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007. [PDF]
7. X. Hou and L. Zhang. Saliency detection: A spectral residual approach.CVPR, 2007. [PDF]
8. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos.CVPR, 2010. [PDF]
9. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics.Journal of Vision, 2008. [PDF]
10. D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes,NIPS, 2004. [PDF]
11. T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look,ICCV, 2009. [PDF]
12. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection.CVPR2011.

Image Classification

• 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]

1. K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features,ICCV2005. [PDF]
2. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,CVPR 2006[PDF]
3. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification,CVPR, 2010 [PDF]
4. J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification,CVPR, 2009 [PDF]
5. M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005. [PDF]
6. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection.ICCV, 2009. [PDF]
7. P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection,ICCV, 2009. [PDF]
8. J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image
   Parsing with Superpixels, ECCV 2010. [PDF]

Category-Independent Object Proposal

• Objectness measure [1] [Code]

• Parametric min-cut [2] [Project]

• Object proposal [3] [Project]

1. B. Alexe, T. Deselaers, V. Ferrari, What is an Object?,CVPR2010 [PDF]
2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation,CVPR2010. [PDF]
3. I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010. [PDF]

MRF

• Graph Cut [Project] [C++/Matlab Wrapper Code]

1. Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 [PDF]

Shadow Detection

• Shadow Detection using Paired Region [Project]

• Ground shadow detection [Project]

1. R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 [PDF]
2. J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs,ECCV2010 [PDF]

Optical Flow

• Kanade-Lucas-Tomasi Feature Tracker [C Code]

• Optical Flow Matlab/C++ code by Ce Liu [Project]

• Horn and Schunck's method by Deqing Sun [Code]

• Black and Anandan's method by Deqing Sun [Code]

• Optical flow code by Deqing Sun [Matlab Code] [Project]

• Large Displacement Optical Flow by Thomas Brox [Executable for 64-bit Linux] [Matlab Mex-functions for 64-bit Linux and 32-bit Windows] [Project]

• Variational Optical Flow by Thomas Brox [Executable for 64-bit Linux] [Executable for 32-bit Windows] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows] [Project]

1. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision,IJCAI1981. [PDF]
2. J. Shi, C. Tomasi, Good Feature to Track, CVPR1994. [PDF]
3. C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis.Doctoral Thesis.MIT 2009. [PDF]
4. B.K.P. Horn and B.G. Schunck, Determining Optical Flow,Artificial Intelligence1981. [PDF]
5. M. J. Black and P. Anandan, A framework for the robust estimation of optical flow,ICCV93. [PDF]
6. D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles,CVPR2010. [PDF]
7. T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation,PAMI, 2010 [PDF]
8. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping,ECCV2004 [PDF]

Object Tracking

• Particle filter object tracking [1] [Project]

• KLT Tracker [2-3] [Project]

• MILTrack [4] [Code]

• Incremental Learning for Robust Visual Tracking [5] [Project]

• Online Boosting Trackers [6-7] [Project]

• L1 Tracking [8] [Matlab code]

1. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic TrackingECCV, 2002. [PDF]
2. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision,IJCAI1981. [PDF]
3. J. Shi, C. Tomasi, Good Feature to Track, CVPR1994. [PDF]
4. B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning,PAMI2011 [PDF]
5. D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking,IJCV2007 [PDF]
6. H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 [PDF]
7. H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust Tracking,ECCV 2008[PDF]
8. X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009. [PDF]

Image Matting

• Closed Form Matting [Code]

• Spectral Matting [Project]

• Learning-based Matting [Code]

1. A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting,PAMI2008 [PDF]
2. A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting.PAMI 2008.[PDF]
3. Y. Zheng and C. Kambhamettu, Learning Based Digital Matting,ICCV2009 [PDF]

Bilateral Filtering

• Fast Bilateral Filter [Project]

• Real-time O(1) Bilateral Filtering [Code]

• SVM for Edge-Preserving Filtering [Code]

1. Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering,
   CVPR 2009. [PDF]
2. Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering,
   CVPR 2010. [PDF]

Image Denoising

• K-SVD [Matlab code]

• BLS-GSM [Project]

• BM3D [Project]

• FoE [Code]

• GFoE [Code]

• Non-local means [Code]

• Kernel regression [Code]

Image Super-Resolution

• MRF for image super-resolution [Project]

• Multi-frame image super-resolution [Project]

• UCSC Super-resolution [Project]

• Sprarse coding super-resolution [Code]

Image Deblurring

• Eficient Marginal Likelihood Optimization in Blind Deconvolution [Code]

• Analyzing spatially varying blur [Project]

• Radon Transform [Code]

Image Quality Assessment

• FSIM [1] [Project]

• Degradation Model [2] [Project]

• SSIM [3] [Project]

• SPIQA [Code]

1. L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment,TIP2011. [PDF]
2. N. Damera-Venkata, and T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik,Image Quality Assessment Based on a Degradation Model,TIP2000. [PDF]
3. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli,Image quality assessment: from error visibility to structuralsimilarity,TIP 2004. [PDF]
4. B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA),ICIP2008. [PDF]
   

Density Estimation

• Kernel Density Estimation Toolbox [Project]

Dimension Reduction

• Dimensionality Reduction Toolbox [Project]

• ISOMAP [Code]

• LLE [Project]

• Laplacian Eigenmaps [Code]

• Diffusion maps [Code]

Sparse Coding

Low-Rank Matrix Completion

Nearest Neighbors matching

• ANN: Approximate Nearest Neighbor Searching [Project] [Matlab wrapper]

• FLANN: Fast Library for Approximate Nearest Neighbors [Project]

Steoreo

• StereoMatcher [Project]

1. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,IJCV2002 [PDF]

Structure from motion

• Boundler [1] [Project]

1. N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3D.SIGGRAPH, 2006. [PDF]

Distance Transformation

• Distance Transforms of Sampled Functions [1] [Project]

1. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions.Technical report, Cornell University, 2004. [PDF]

Chamfer Matching

• Fast Directional Chamfer Matching [Code]

1. M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer Matching,CVPR2010 [PDF]

Clustering

• K-Means [VLFeat] [Oxford code]

• Spectral Clustering [UW Project][Code] [Self-Tuning code]

• Affinity Propagation [Project]

Classification

• SVM [Libsvm] [SVM-Light] [SVM-Struct]

• Boosting

• Naive Bayes

Regression

• SVM

• RVM

• GPR

Multiple Kernel Learning (MKL)

• SHOGUN [Project]

• OpenKernel.org [Project]

• DOGMA (online algorithms) [Project]

• SimpleMKL [Project]
  

1. S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning.JMLR, 2006. [PDF]
2. F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning.ICML, 2011. [PDF]
   
3. F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning.CVPR, 2010. [PDF]
   
4. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl.JMRL, 2008. [PDF]

Multiple Instance Learning (MIL)

• MIForests [1] [Project]

• MILIS [2]

• MILES [3] [Project] [Code]

• DD-SVM [4] [Project]

1. C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees,ECCV2010. [PDF]
2. Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection,PAMI2010. [PDF]
3. Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection.PAMI2006 [PDF]
4. Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions,JMLR2004. [PDF]
   

Other Utilities

• Code for downloading Flickr images, by James Hays [Code]

• The Lightspeed Matlab Toolbox by Tom Minka [Code]

• MATLAB Functions for Multiple View Geometry [Code]

• Peter's Functions for Computer Vision [Code]

• Statistical Pattern Recognition Toolbox [Code]

Useful Links (dataset, lectures, and other softwares)

Conference Information

• Computer Image Analysis, Computer Vision Conferences

Papers

• Computer vision paper on the web

• NIPS Proceedings

Datasets

• Compiled list of recognition datasets

• Computer vision dataset from CMU

Lectures

• Videolectures

Source Codes

• Computer Vision Algorithm Implementations

• OpenCV

• Source Code Collection for ReproducibleResearch