Topic
Resources
References
Feature Extraction
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
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 ]
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 ]
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 ]
Global Contrast based Salient Region Detection [12] [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 . InNIPS , 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 ]
M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection . CVPR 2011.
Image Classification
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
Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 [PDF ]
Shadow Detection
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
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
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
Image Quality Assessment
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
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 [PDF ]
Structure from motion
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 ]
Chamfer Matching
Fast Directional Chamfer Matching [Code ]
M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer Matching , CVPR 2010 [PDF ]
Clustering
Classification
Regression
Multiple Kernel Learning (MKL)
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)
C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees , ECCV 2010. [PDF ]
Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection , PAMI 2010. [PDF ]
Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection . PAMI 2006 [PDF ]
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 ]
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