计算机视觉代码合集
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Topic |
Resources |
References |
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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] |
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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] |
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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. 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] |
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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. |
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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
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Topic |
Resources |
References |
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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] |
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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] |
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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] |
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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] |
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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] |
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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] |
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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,
2. Q.
Yang, S. Wang, and N.
Ahuja, SVM
for Edge-Preserving Filtering, |
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Image Denoising |
· K-SVD [Matlab code] · BLS-GSM [Project] · BM3D [Project] · FoE [Code] · GFoE [Code] · Non-local means [Code] · Kernel regression [Code] |
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Image Super-Resolution |
· MRF for image super-resolution [Project] · Multi-frame image super-resolution [Project] · UCSC Super-resolution [Project] · Sprarse coding super-resolution [Code] |
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Image Deblurring |
· Eficient Marginal Likelihood Optimization in Blind Deconvolution [Code] · Analyzing spatially varying blur [Project] · Radon Transform [Code] |
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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] |
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Density Estimation |
· Kernel Density Estimation Toolbox [Project] |
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Dimension Reduction |
· Dimensionality Reduction Toolbox [Project] · ISOMAP [Code] · LLE [Project] · Laplacian Eigenmaps [Code] · Diffusion maps [Code] |
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Sparse Coding |
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Low-Rank Matrix Completion |
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Nearest Neighbors matching |
· ANN: Approximate Nearest Neighbor Searching [Project] [Matlab wrapper] · FLANN: Fast Library for Approximate Nearest Neighbors [Project] |
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Steoreo |
· StereoMatcher [Project] |
1. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2002 [PDF] |
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Structure from motion |
· Boundler [1] [Project]
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1. N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH, 2006. [PDF] |
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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] |
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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] |
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Clustering |
· K-Means [VLFeat] [Oxford code] · Spectral Clustering [UW Project][Code] [Self-Tuning code] · Affinity Propagation [Project] |
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Classification |
· SVM [Libsvm] [SVM-Light] [SVM-Struct] · Boosting · Naive Bayes |
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Regression |
· SVM · RVM · GPR |
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Topic |
Resources |
References |
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Multiple Kernel Learning (MKL) |
? SHOGUN [Project] ? OpenKernel.org [Project] ? DOGMA (online algorithms) [Project] ? SimpleMKL [Project] |
1. S. Sonnenburg, G. R?tsch, C. Sch?fer, B. Sch?lkopf . 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] |
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Multiple Instance Learning (MIL) |
? MIForests [1] [Project] ? MILIS [2] ? DD-SVM [4] [Project] |
1. C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010. [PDF] 2. Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010. [PDF] 3. Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 [以上是关于计算机视觉代码合集的主要内容，如果未能解决你的问题，请参考以下文章 |