计算机视觉代码合集

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. CVPR 2010. [PDF]

10.  N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [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 using shape 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, CVPR 2011. [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, IJCV 2004. [PDF]

4.    D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002. [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, CVPR 2011

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,” PAMI 1996 [PDF]

13.  M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 [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. CVPR 2005. [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. CVPR 2010 [PDF]

4.    L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 [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, CVPR 2001. [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. In CVPR, 2009. [PDF]

3.    R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 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. In CVPR, 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. CVPR 2011.

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, ICCV 2005. [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]

Topic

Resources

References

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?, CVPR 2010 [PDF]

2.    J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010. [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, ECCV 2010 [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, IJCAI 1981. [PDF]

2.    J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [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 Intelligence 1981. [PDF]

5.    M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. [PDF]

6.    D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, CVPR 2010. [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, ECCV 2004 [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 Tracking ECCV, 2002. [PDF]

2.    B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF]

3.    J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF]

4.    B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning, PAMI 2011 [PDF]

5.    D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 [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, PAMI 2008 [PDF]

2.    A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008. [PDF]

3.    Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 [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, TIP 2011. [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, TIP 2000. [PDF]

3.    Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. [PDF]

4.    B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA), ICIP 2008. [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, IJCV 2002 [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, CVPR 2010 [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

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 Reproducible Research