• Computer Vision Resources

    Computer Vision Resources

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    Topic

    Resources

    References

    Feature Extraction
    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
    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]

    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]
    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. In NIPS, 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]

    Image Classification
    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]

    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
    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
    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

    		  

    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
    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

    		  

    Sparse Coding

    		    

    Low-Rank Matrix Completion

    		    

    Nearest Neighbors matching

    		  

    Steoreo

    1. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2002 [PDF]

    Structure from motion
    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]

    Clustering

    		  

    Classification

    		  

    Regression

    • SVM

    • RVM

    • GPR

    		  

    Multiple Kernel Learning (MKL)
    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)

    		    

    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

    Papers

    Datasets

    Lectures

    Source Codes
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  • 原文地址：https://www.cnblogs.com/gujianhan/p/4019646.html
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