Computer Vision Resources

Computer Vision Resources

Softwares

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]	D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. [PDF] Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004. [PDF] J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009. [PDF] H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006. [PDF] 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] J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002. [PDF] A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005. [PDF] E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007. [PDF] T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010. [PDF] N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [PDF] A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001. [PDF] S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002. [PDF] K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010. I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF] 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]	J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 [PDF] X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003. [PDF] P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004. [PDF] D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002. [PDF] P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011. [PDF] 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] A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008. [PDF] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010. [PDF] A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007. [PDF] S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,”  ACCV 2009. [PDF] 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]	N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [PDF] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 [PDF] P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR 2010 [PDF] L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 [PDF] B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008. [PDF] 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]	L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998. [PDF] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. [PDF] R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010. [PDF] N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005. [PDF] S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. [PDF] J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007. [PDF] X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007. [PDF] E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010. [PDF] 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] D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004. [PDF] T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009. [PDF]
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]	K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005. [PDF] S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 [PDF] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 [PDF] J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 [PDF] M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005. [PDF] A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009. [PDF] P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009. [PDF] 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]	B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 [PDF] J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010. [PDF] I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010. [PDF]
MRF	Graph Cut [Project] [C++/Matlab Wrapper Code]	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]	R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 [PDF] 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]	B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF] J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF] C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. MIT 2009. [PDF] B.K.P. Horn and B.G. Schunck, Determining Optical Flow, Artificial Intelligence 1981. [PDF] M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. [PDF] D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, CVPR 2010. [PDF] T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI, 2010 [PDF] 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]	P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic Tracking ECCV, 2002. [PDF] B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF] J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF] B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning, PAMI 2011 [PDF] D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 [PDF] H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 [PDF] H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust Tracking, ECCV 2008 [PDF] 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]	A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008 [PDF] A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008. [PDF] 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]	Q. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering,  CVPR 2009. [PDF] 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]	L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment, TIP 2011. [PDF] 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] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. [PDF] 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]	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]	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]	P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions. Technical report, Cornell University, 2004. [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]	S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006. [PDF] F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011. [PDF] F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010. [PDF] 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	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