图像处理中项目代码合集,包括特征提取-图像分割-分类-匹配-降噪等等

       这几天在研究血管增强与分割,发现一个比较全面的图像处理方面的项目集合,里面涵盖了特征提取、图像分割、图像分类、图像匹配、图像降噪,光流法等等方面的项目和代码集合,项目是2012年之前的,但是涵盖比较基础的原理知识,用到的时候可以参考一下:

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 comparisonSIAM Journal on Imaging Sciences, 2009. [PDF]
  4. H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust FeaturesECCV, 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 detectorsIJCV, 2005. [PDF]
  6. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regionsBMVC, 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 DetectionCVPR 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 envelopeIJCV, 2001. [PDF]
  12. S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contextsPAMI, 2002. [PDF]
  13. K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene RecognitionPAMI, 2010.
  14. I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF]
  15. J. Kim and K. Grauman, Boundary Preserving Dense Local RegionsCVPR 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 SegmentationPAMI, 2000 [PDF]
  2. X. Ren and J. Malik. Learning a classification model for segmentationICCV, 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 AnalysisPAMI 2002. [PDF]
  5. P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image SegmentationPAMI, 2011. [PDF]
  6. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric FlowsPAMI 2009. [PDF]
  7. A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode SeekingECCV, 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 CompressionCVIU, 2007. [PDF]
  10. S. Maji, N. Vishnoi and J. Malik, Biased Normalized CutCVPR 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 ModelsPAMI, 2010 [PDF]
  3. P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part ModelsCVPR 2010 [PDF]
  4. L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsICCV 2009 [PDF]
  5. B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and SegmentationIJCV, 2008. [PDF]
  6. P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple FeaturesCVPR 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 analysisPAMI, 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. In NIPS, 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 approachCVPR, 2007. [PDF]
  8. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videosCVPR, 2010. [PDF]
  9. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statisticsJournal of Vision, 2008. [PDF]
  10. D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered ScenesNIPS, 2004. [PDF]
  11. T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans LookICCV, 2009. [PDF]
  12. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region DetectionCVPR 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 FeaturesICCV 2005. [PDF]
  2. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene CategoriesCVPR 2006 [PDF]
  3. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image ClassificationCVPR, 2010 [PDF]
  4. J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image ClassificationCVPR, 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 DetectionICCV, 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?CVPR 2010 [PDF]
  2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object SegmentationCVPR 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 PhotographsECCV 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 VisionIJCAI 1981. [PDF]
  2. J. Shi, C. Tomasi, Good Feature to TrackCVPR 1994. [PDF]
  3. C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral ThesisMIT 2009. [PDF]
  4. B.K.P. Horn and B.G. Schunck, Determining Optical FlowArtificial Intelligence1981. [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 principlesCVPR 2010. [PDF]
  7. T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimationPAMI, 2010 [PDF]
  8. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warpingECCV 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 VisionIJCAI 1981. [PDF]
  3. J. Shi, C. Tomasi, Good Feature to TrackCVPR 1994. [PDF]
  4. B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance LearningPAMI 2011 [PDF]
  5. D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual TrackingIJCV 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 TrackingECCV 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. WeissA Closed Form Solution to Natural Image MattingPAMI 2008 [PDF]
  2. A. Levin, A. Rav-Acha, D. Lischinski. Spectral MattingPAMI 2008. [PDF]
  3. Y. Zheng and C. Kambhamettu, Learning Based Digital MattingICCV 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 AssessmentTIP 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 ModelTIP 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 algorithmsIJCV 2002 [PDF]

Structure from motion

  • Boundler [1] [Project]

 

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

Distance Transformation

  • Distance Transforms of Sampled Functions [1] [Project]
  1. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functionsTechnical 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 MatchingCVPR 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

 

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 learningJMLR, 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 learningCVPR, 2010. [PDF]
  4. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimplemklJMRL, 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 TreesECCV 2010. [PDF]
  2. Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selectionPAMI 2010. [PDF]
  3. Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance SelectionPAMI 2006 [PDF]
  4. Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with RegionsJMLR 2004. [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 Reproducible Research


一、特征提取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][Project] [Code]

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

  • Weighted Histogram[Code]

  • Histogram-based Interest Points Detectors[Paper][Code]

  • An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]

  • Fast Sparse Representation with Prototypes[Project]

  • Corner Detection [Project]

  • AGAST Corner Detector: faster than FAST and even FAST-ER[Project]

  • Real-time Facial Feature Detection using Conditional Regression Forests[Project]

  • Global and Efficient Self-Similarity for Object Classification and Detection[code]

  • WαSH: Weighted α-Shapes for Local Feature Detection[Project]

  • HOG[Project]

  • Online Selection of Discriminative Tracking Features[Project]


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:

  • Kinect toolbox[Project]

  • OpenNI[Project]

  • zouxy09 CSDN Blog[Resource]

  • FingerTracker 手指跟踪[code]


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[

  • A Hough Transform-Based Voting Framework for Action Recognition[

  • Motion Interchange Patterns for Action Recognition in Unconstrained Videos[

  • 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 [


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:

    • SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT

    • SURF: Herbert Bay’s code, OpenCV, GPU-SURF


    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

     

    • Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)

    • Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.

    • Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact same performance of deformable part-based detectors but with significant acceleration (ECCV 2012).

    • Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).

    • Poselets – C++ and Matlab versions for object detection based on poselets.

    • Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).

       

      Attributes and Semantic Features

      • Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).

      • Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank

      • Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

      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

      • See Part-based Models and Convolutional Nets above.

      • Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).

      • Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.

      • OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.

      • Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

      3D Recognition

      • Point-Cloud Library – Library for 3D image and point cloud processing.

      Action Recognition

      • ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).

      • STIP Features – software for computing space-time interest point descriptors

      • Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)

      • Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)


      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.

      • [url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – 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

      • Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.

      • Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.

      • Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.

      • Leeds Butterfly Dataset – 832 images of 10 species of butterflies.

      • Oxford Flower Dataset – Hundreds of flower categories.

      Face Detection

      • [url=http://vis-www.cs.umass.edu/fddb/]FDDB[/url] – 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.

      • [url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – 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

      • Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.

      • INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.

      • ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.

      • TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.

      • PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.

      • USC Pedestrian Dataset – Small dataset captured from surveillance cameras.

      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

      • MIT SUN Dataset – MIT scene understanding dataset.

      • UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.

      Feature Detection and Description

      • VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor an evaluation framework.

      Action Recognition

      • Benchmarking Activity Recognition – CVPR 2012 tutorial covering various datasets for action recognition.

      RGBD Recognition

      • RGB-D Object Dataset – Dataset containing 300 common household objects

      Reference:

       

      [1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html


      特征提取
      • SURF特征: http://www.vision.ee.ethz.ch/software/index.de.html(当然这只是其中之一)

      • LBP特征(一种纹理特征):http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html

      • Fast Corner Detection(OpenCV中的Fast算法):FAST Corner Detection -- Edward Rosten

      机器视觉
      • A simple object detector with boosting(Awarded the Best Short Course Prize at ICCV 2005,So了解adaboost的推荐之作):http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html

      • Boosting(该网页上有相当全的Boosting的文章和几个Boosting代码,本人推荐):http://cbio.mskcc.org/~aarvey/boosting_papers.html

      • Adaboost Matlab 工具:http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox

      • MultiBoost(不说啥了,多类Adaboost算法的程序):http://sourceforge.net/projects/multiboost/

      • TextonBoost(我们教研室王冠夫师兄的毕设): Jamie Shotton - Code

      • LibSvm的老爹(推荐): http://www.csie.ntu.edu.tw/~cjlin/

      • Conditional Random Fields(CRF论文+Code列表,推荐)

      • CRF++: Yet Another CRF toolkit

      • Conditional Random Field (CRF) Toolbox for Matlab

      • Tree CRFs

      • LingPipe: Installation

      • Hidden Markov Models(推荐)

      • 隐马尔科夫模型(Hidden Markov Models)系列之一 - eaglex的专栏 - 博客频道 - CSDN.NET(推荐)

      综合代码
      • CvPapers(好吧,牛吧网站,里面有ICCV,CVPR,ECCV,SIGGRAPH的论文收录,然后还有一些论文的代码搜集,要求加精!):http://www.cvpapers.com/

      • Computer Vision Software(里面代码很多,并详细的给出了分类):http://peipa.essex.ac.uk/info/software.html

      • 某人的Windows Live(我看里面东东不少就收藏了):https://skydrive.live.com/?cid=3b6244088fd5a769#cid=3B6244088FD5A769&id=3B6244088FD5A769!523

      • MATLAB and Octave Functions for Computer Vision and Image Processing(这个里面的东西也很全,只是都是用Matlab和Octave开发的):http://www.csse.uwa.edu.au/~pk/research/matlabfns/

      • Computer Vision Resources(里面的视觉算法很多,给出了相应的论文和Code,挺好的):https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

      • MATLAB Functions for Multiple View Geometry(关于物体多视角计算的库):http://www.robots.ox.ac.uk/~vgg/hzbook/code/

      • Evolutive Algorithm based on Naïve Bayes models Estimation(单独列了一个算法的Code):http://www.cvc.uab.cat/~xbaro/eanbe/#_Software

      主页代码
      • Pablo Negri's Home Page

      • Jianxin Wu's homepage

      • Peter Carbonetto

      • Markov Random Fields for Super-Resolution

      • Detecting and Sketching the Common

      • Pedro Felzenszwalb

      • Hae JONG, SEO

      • CAP 5416 - Computer Vision

      • Parallel Tracking and Mapping for Small AR Workspaces (PTAM)

      • Deva Ramanan - UC Irvine - Computer Vision

      • Raghuraman Gopalan

      • Hui Kong

      • Jamie Shotton - Post-Doctoral Researcher in Computer Vision

      • Jean-Yves AUDIBERT

      • Olga Veksler

      • Stephen Gould

      • Publications (Last Update: 09/30/10)

      • Karim Ali - FlowBoost

      • A simple parts and structure object detector

      • Code - Oxford Brookes Vision Group

      • Taku Kudo

      行人检测
      • Histogram of Oriented Gradient (Windows)

      • INRIA Pedestrian detector

      • Poselets

      • William Robson Schwartz - Softwares

      • calvin upper-body detector v1.02

      • RPT@CVG

      • Main Page

      • Source Code

      • Dr. Luciano Spinello

      • Pedestrian Detection

      • Class-Specific Hough Forests for Object Detection

      • Jianxin Wu's homepage(就是上面的)

      • Berkeley大学做的Pedestrian Detector,使用交叉核的支持向量机,特征使用HOG金字塔,提供Matlab和C++混编的代码:http://www.cs.berkeley.edu/~smaji/projects/ped-detector/

      视觉壁障
      • High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning

      • TLD(2010年很火的tracking算法)

      • online boosting trackers

      • Boris Babenko

      • Optical Flow Algorithm Evaluation (提供了一个动态贝叶斯网络框架,例如递 归信息处理与分析、卡尔曼滤波、粒子滤波、序列蒙特卡罗方法等,C++写的)http://of-eval.sourceforge.net/

      物体检测算法
      • Object Detection

      • Software for object detection

      人脸检测
      • Source Code

      • 10个人脸检测项目

      • Jianxin Wu's homepage(又是这货)

      ICA独立成分分析
      • An ICA page-papers,code,demo,links (Tony Bell)

      • FastICA

      • Cached k-d tree search for ICP algorithms

      滤波算法
      • 卡尔曼滤波:The Kalman Filter(终极网页)

      • Bayesian Filtering Library: The Bayesian Filtering Library

      路面识别
      • Source Code

      • Vanishing point detection for general road detection

      分割算法
      • MATLAB Normalized Cuts Segmentation Code:software

      • 超像素分割:SLIC Superpixels

以上是从下面网址中汇总来的:

http://www.360doc.com/content/12/0201/11/8703626_183332994.shtml

https://www.cnblogs.com/findumars/p/5009003.html

另外,在http://blog.csdn.net/zouxy09/article/details/8550952里也给出了一些项目链接汇总。


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