Topic | Resources | References |
Feature Extraction | -
SIFT [1] [][] [] -
PCA-SIFT [2] [] -
Affine-SIFT [3] [] -
SURF [4] [] [] -
Affine Covariant Features [5] [] -
MSER [6] [] [] -
Geometric Blur [7] [] -
Local Self-Similarity Descriptor [8] [] -
Global and Efficient Self-Similarity [9] [] -
Histogram of Oriented Graidents [10] [] [] -
GIST [11] [] -
Shape Context [12] [] -
Color Descriptor [13] [Project] -
Pyramids of Histograms of Oriented Gradients [] -
Space-Time Interest Points (STIP) [14] [] -
Boundary Preserving Dense Local Regions [15][] | - D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. []
- Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004. []
- J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009. []
- H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006. []
- 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. []
- J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002. []
- A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005. []
- E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007. []
- T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010. []
- N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection.CVPR 2005. []
- A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001. []
- S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002. []
- 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. []
- J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011. []
|
Image Segmentation | -
Normalized Cut [1] [] -
Gerg Mori' Superpixel code [2] [] -
Efficient Graph-based Image Segmentation [3] [] [] -
Mean-Shift Image Segmentation [4] [] [] -
OWT-UCM Hierarchical Segmentation [5] [] -
Turbepixels [6] [] [] [] -
Quick-Shift [7] [] -
SLIC Superpixels [8] [] -
Segmentation by Minimum Code Length [9] [] -
Biased Normalized Cut [10] [] -
Segmentation Tree [11-12] [] -
Entropy Rate Superpixel Segmentation [13] [] | - J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 []
- X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003. []
- P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation,IJCV 2004. []
- D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002. []
- P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011. []
- A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009. []
- A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008. []
- R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010. []
- A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007. []
- 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. []
- N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 []
- M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 []
|
Object Detection | -
A simple object detector with boosting [] -
INRIA Object Detection and Localization Toolkit [1] [] -
Discriminatively Trained Deformable Part Models [2] [] -
Cascade Object Detection with Deformable Part Models [3] [] -
Poselet [4] [] -
Implicit Shape Model [5] [] - Viola and Jones's Face Detection [6] []
| - N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection.CVPR 2005. []
- P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 []
- P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR 2010 []
- L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 []
- B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008. []
- P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR 2001. []
|
Saliency Detection | -
Itti, Koch, and Niebur' saliency detection [1] [] -
Frequency-tuned salient region detection [2] [] -
Saliency detection using maximum symmetric surround [3] [] -
Attention via Information Maximization [4] [] -
Context-aware saliency detection [5] [] -
Graph-based visual saliency [6] [] -
Saliency detection: A spectral residual approach. [7] [] -
Segmenting salient objects from images and videos. [8] [] -
Saliency Using Natural statistics. [9] [] -
Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [] -
Learning to Predict Where Humans Look [11] [] - Global Contrast based Salient Region Detection [12] []
| - L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998. []
- R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. []
- R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010. []
- N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005. []
- S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. InCVPR, 2010. []
- J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007. []
- X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007. []
- E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010. []
- L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008. []
- D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004. []
- T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009. []
- 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] [] -
Spatial Pyramid Matching [2] [] -
Locality-constrained Linear Coding [3] [] [] -
Sparse Coding [4] [] [] -
Texture Classification [5] [] -
Multiple Kernels for Image Classification [6] [] -
Feature Combination [7] [] - SuperParsing []
| - K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005. []
- S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 []
- J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 []
- J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 []
- M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005. []
- A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009. []
- P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009. []
- J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric ImageParsing with Superpixels, ECCV 2010. []
|
Category-Independent Object Proposal | | - B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 []
- J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010. []
- I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010. []
|
MRF | | - Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 []
|
Shadow Detection | | - R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 []
- J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 []
|
Optical Flow | -
Kanade-Lucas-Tomasi Feature Tracker [] -
Optical Flow Matlab/C++ code by Ce Liu [] -
Horn and Schunck's method by Deqing Sun [] -
Black and Anandan's method by Deqing Sun [] -
Optical flow code by Deqing Sun [] [] -
Large Displacement Optical Flow by Thomas Brox [] [ ] [] -
Variational Optical Flow by Thomas Brox [] [ ] [ ] [] | - B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. []
- J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. []
- C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. MIT 2009. []
- B.K.P. Horn and B.G. Schunck, Determining Optical Flow, Artificial Intelligence1981. [PDF]
- M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. []
- D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, CVPR 2010. []
- T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI, 2010 []
- T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 []
|
Object Tracking | -
Particle filter object tracking [1] [] -
KLT Tracker [2-3] [] -
MILTrack [4] [] -
Incremental Learning for Robust Visual Tracking [5] [] -
Online Boosting Trackers [6-7] [] -
L1 Tracking [8] [] | - P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic Tracking ECCV, 2002. []
- B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. []
- J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. []
- B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning, PAMI 2011 []
- D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 []
- H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 []
- H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust Tracking, ECCV 2008 []
- X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009. []
|
Image Matting | | - A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008 []
- A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008. []
- Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 []
|
Bilateral Filtering | | - Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009. []
- Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010. []
|
Image Denoising | -
K-SVD [] -
BLS-GSM [] -
BM3D [] -
FoE [] -
GFoE [] -
Non-local means [] -
Kernel regression [] | |
Image Super-Resolution | -
MRF for image super-resolution [] -
Multi-frame image super-resolution [] -
UCSC Super-resolution [] -
Sprarse coding super-resolution [] | |
Image Deblurring | | |
Image Quality Assessment | -
FSIM [1] [] -
Degradation Model [2] [] -
SSIM [3] [] -
SPIQA [] | - L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment, TIP 2011. []
- 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. []
- Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. []
- B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA), ICIP 2008. []
|
Density Estimation | - Kernel Density Estimation Toolbox []
| |
Dimension Reduction | | |
Sparse Coding | | |
Low-Rank Matrix Completion | | |
Nearest Neighbors matching | | |
Steoreo | | - D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2002 []
|
Structure from motion | | - N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH, 2006. []
|
Distance Transformation | - Distance Transforms of Sampled Functions [1] []
| - P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions. Technical report, Cornell University, 2004. []
|
Chamfer Matching | - Fast Directional Chamfer Matching []
| - M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer Matching, CVPR 2010 []
|
Clustering | | |
Classification | -
SVM [] [] [] -
Boosting -
Naive Bayes | |
Regression | | |
Multiple Kernel Learning (MKL) | | - S. Sonnenburg, G. R?tsch, C. Sch?fer, B. Sch?lkopf . Large scale multiple kernel learning. JMLR, 2006. []
- F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011. []
- F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010. []
- A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008. []
|
Multiple Instance Learning (MIL) | -
MIForests [1] [] -
MILIS [2] -
MILES [3] [] [] -
DD-SVM [4] [] | - C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010. []
- Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010. []
- Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 []
- Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004. []
|
Other Utilities | -
Code for downloading Flickr images, by James Hays [] -
The Lightspeed Matlab Toolbox by Tom Minka [] -
MATLAB Functions for Multiple View Geometry [] -
Peter's Functions for Computer Vision [] - Statistical Pattern Recognition Toolbox []
| |