Topic

Resources

References

Feature Extraction

• SIFT [1] [Demo program][SIFT Library] [VLFeat]

• PCA-SIFT [2] [Project]

• Affine-SIFT [3] [Project]

• SURF [4] [OpenSURF] [Matlab Wrapper]

• Affine Covariant Features [5] [Oxford project]

• MSER [6] [Oxford project] [VLFeat]

• Geometric Blur [7] [Code]

• Local Self-Similarity Descriptor [8] [Oxford implementation]

• Global and Efficient Self-Similarity [9] [Code]

• Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]

• GIST [11] [Project]

• Shape Context [12] [Project]

• Color Descriptor [13] [Project]

• Pyramids of Histograms of Oriented Gradients [Code]

• Space-Time Interest Points (STIP) [14] [Code]

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

1. D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. [PDF]
2. Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004. [PDF]
3. J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009. [PDF]
4. H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features,ECCV, 2006. [PDF]
5. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, A comparison of affine region detectors. IJCV, 2005. [PDF]
6. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002. [PDF]
7. A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005. [PDF]
8. E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007. [PDF]
9. T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010. [PDF]
10. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [PDF]
11. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001. [PDF]
12. S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002. [PDF]
13. K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010.
14. I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF]
15. J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011. [PDF]

Image Segmentation

• Normalized Cut [1] [Matlab code]

• Gerg Mori' Superpixel code [2] [Matlab code]

• Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]

• Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]

• OWT-UCM Hierarchical Segmentation [5] [Resources]

• Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]

• Quick-Shift [7] [VLFeat]

• SLIC Superpixels [8] [Project]

• Segmentation by Minimum Code Length [9] [Project]

• Biased Normalized Cut [10] [Project]

• Segmentation Tree [11-12] [Project]

• Entropy Rate Superpixel Segmentation [13] [Code]

1. J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 [PDF]
2. X. Ren and J. Malik. Learning a classification model for segmentation.ICCV, 2003. [PDF]
3. P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004. [PDF]
4. D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002. [PDF]
5. P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011. [PDF]
6. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009. [PDF]
7. A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking,ECCV, 2008. [PDF]
8. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010. [PDF]
9. A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007. [PDF]
10. S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011
11. E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,”  ACCV 2009. [PDF]
12. N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 [PDF]
13. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 [PDF]

Object Detection

• A simple object detector with boosting [Project]

• INRIA Object Detection and Localization Toolkit [1] [Project]

• Discriminatively Trained Deformable Part Models [2] [Project]

• Cascade Object Detection with Deformable Part Models [3] [Project]

• Poselet [4] [Project]

• Implicit Shape Model [5] [Project]

• Viola and Jones's Face Detection [6] [Project]

1. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [PDF]
2. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
   Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 [PDF]
3. P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR 2010 [PDF]
4. L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 [PDF]
5. B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008. [PDF]
6. P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR 2001. [PDF]

Saliency Detection

• Itti, Koch, and Niebur' saliency detection [1] [Matlab code]

• Frequency-tuned salient region detection [2] [Project]

• Saliency detection using maximum symmetric surround [3] [Project]

• Attention via Information Maximization [4] [Matlab code]

• Context-aware saliency detection [5] [Matlab code]

• Graph-based visual saliency [6] [Matlab code]

• Saliency detection: A spectral residual approach. [7] [Matlab code]

• Segmenting salient objects from images and videos. [8] [Matlab code]

• Saliency Using Natural statistics. [9] [Matlab code]

• Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]

• Learning to Predict Where Humans Look [11] [Project]

• Global Contrast based Salient Region Detection [12] [Project]

1. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998. [PDF]
2. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. [PDF]
3. R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010. [PDF]
4. N. Bruce and J. Tsotsos. Saliency based on information maximization. InNIPS, 2005. [PDF]
5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. [PDF]
6. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007. [PDF]
7. X. Hou and L. Zhang. Saliency detection: A spectral residual approach.CVPR, 2007. [PDF]
8. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010. [PDF]
9. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008. [PDF]
10. D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004. [PDF]
11. T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009. [PDF]
12. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR 2011.

Image Classification

• Pyramid Match [1] [Project]

• Spatial Pyramid Matching [2] [Code]

• Locality-constrained Linear Coding [3] [Project] [Matlab code]

• Sparse Coding [4] [Project] [Matlab code]

• Texture Classification [5] [Project]

• Multiple Kernels for Image Classification [6] [Project]

• Feature Combination [7] [Project]

• SuperParsing [Code]

1. K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005. [PDF]
2. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006[PDF]
3. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 [PDF]
4. J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 [PDF]
5. M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005. [PDF]
6. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009. [PDF]
7. P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009. [PDF]
8. J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image
   Parsing with Superpixels, ECCV 2010. [PDF]

Category-Independent Object Proposal

• Objectness measure [1] [Code]

• Parametric min-cut [2] [Project]

• Object proposal [3] [Project]

1. B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 [PDF]
2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010. [PDF]
3. I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010. [PDF]

MRF

• Graph Cut [Project] [C++/Matlab Wrapper Code]

1. Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 [PDF]

Shadow Detection

• Shadow Detection using Paired Region [Project]

• Ground shadow detection [Project]

1. R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 [PDF]
2. J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 [PDF]

Optical Flow

• Kanade-Lucas-Tomasi Feature Tracker [C Code]

• Optical Flow Matlab/C++ code by Ce Liu [Project]

• Horn and Schunck's method by Deqing Sun [Code]

• Black and Anandan's method by Deqing Sun [Code]

• Optical flow code by Deqing Sun [Matlab Code] [Project]

• Large Displacement Optical Flow by Thomas Brox [Executable for 64-bit Linux] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows] [Project]

• Variational Optical Flow by Thomas Brox [Executable for 64-bit Linux] [ Executable for 32-bit Windows ] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows ] [Project]

1. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF]
2. J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF]
3. C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. MIT 2009. [PDF]
4. B.K.P. Horn and B.G. Schunck, Determining Optical Flow, Artificial Intelligence 1981. [PDF]
5. M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. [PDF]
6. D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, CVPR 2010. [PDF]
7. T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI, 2010 [PDF]
8. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 [PDF]

Object Tracking

• Particle filter object tracking [1] [Project]

• KLT Tracker [2-3] [Project]

• MILTrack [4] [Code]

• Incremental Learning for Robust Visual Tracking [5] [Project]

• Online Boosting Trackers [6-7] [Project]

• L1 Tracking [8] [Matlab code]

1. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic Tracking ECCV, 2002. [PDF]
2. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF]
3. J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF]
4. B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning, PAMI 2011 [PDF]
5. D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 [PDF]
6. H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 [PDF]
7. H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust Tracking, ECCV 2008 [PDF]
8. X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009. [PDF]

Image Matting

• Closed Form Matting [Code]

• Spectral Matting [Project]

• Learning-based Matting [Code]

1. A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008 [PDF]
2. A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008. [PDF]
3. Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 [PDF]

Bilateral Filtering

• Fast Bilateral Filter [Project]

• Real-time O(1) Bilateral Filtering [Code]

• SVM for Edge-Preserving Filtering [Code]

1. Q. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering, 
   CVPR 2009. [PDF]
2. Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, 
   CVPR 2010. [PDF]

Image Denoising

• K-SVD [Matlab code]

• BLS-GSM [Project]

• BM3D [Project]

• FoE [Code]

• GFoE [Code]

• Non-local means [Code]

• Kernel regression [Code]

Image Super-Resolution

• MRF for image super-resolution [Project]

• Multi-frame image super-resolution [Project]

• UCSC Super-resolution [Project]

• Sprarse coding super-resolution [Code]

Image Deblurring

• Eficient Marginal Likelihood Optimization in Blind Deconvolution [Code]

• Analyzing spatially varying blur [Project]

• Radon Transform [Code]

Image Quality Assessment

• FSIM [1] [Project]

• Degradation Model [2] [Project]

• SSIM [3] [Project]

• SPIQA [Code]

1. L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment, TIP 2011. [PDF]
2. N. Damera-Venkata, and T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik,Image Quality Assessment Based on a Degradation Model, TIP 2000. [PDF]
3. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. [PDF]
4. B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA), ICIP 2008. [PDF]

Density Estimation

• Kernel Density Estimation Toolbox [Project]

Dimension Reduction

• Dimensionality Reduction Toolbox [Project]

• ISOMAP [Code]

• LLE [Project]

• Laplacian Eigenmaps [Code]

• Diffusion maps [Code]

Sparse Coding

Low-Rank Matrix Completion

Nearest Neighbors matching

• ANN: Approximate Nearest Neighbor Searching [Project] [Matlab wrapper]

• FLANN: Fast Library for Approximate Nearest Neighbors [Project]

Steoreo

• StereoMatcher [Project]

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

Structure from motion

• Boundler [1] [Project]

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

Distance Transformation

• Distance Transforms of Sampled Functions [1] [Project]

1. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions. Technical report, Cornell University, 2004. [PDF]

Chamfer Matching

• Fast Directional Chamfer Matching [Code]

1. M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer Matching, CVPR 2010 [PDF]

Clustering

• K-Means [VLFeat] [Oxford code]

• Spectral Clustering [UW Project][Code] [Self-Tuning code]

• Affinity Propagation [Project]

Classification

• SVM [Libsvm] [SVM-Light] [SVM-Struct]

• Boosting

• Naive Bayes

Regression

• SVM

• RVM

• GPR

Multiple Kernel Learning (MKL)

• SHOGUN [Project]

• OpenKernel.org [Project]

• DOGMA (online algorithms) [Project]

• SimpleMKL [Project]

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)

• 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 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 [PDF]
4. Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 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]

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