Topic | Name | Reference | Link |
|---|
| Feature Detection,Feature Extraction, and Action Recognition |
Space-Time Interest Points (STIP) |
I. Laptev, On Space-Time Interest Points, IJCV, 2005 and I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005 |
http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zipandhttp://www.nada.kth.se/cvap/abstracts/cvap284.html |
| Action Recognition |
3D Gradients (HOG3D) |
A. Klaser, M. Marsza?ek, and C. Schmid, BMVC, 2008. |
http://lear.inrialpes.fr/people/klaeser/research_hog3d |
| Action Recognition |
Dense Trajectories Video Description |
H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011 |
http://lear.inrialpes.fr/people/wang/dense_trajectories |
| Alpha Matting |
Spectral Matting |
A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008 |
http://www.vision.huji.ac.il/SpectralMatting/ |
| Alpha Matting |
Shared Matting |
E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010 |
http://www.inf.ufrgs.br/~eslgastal/SharedMatting/ |
| Alpha Matting |
Bayesian Matting |
Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001 |
http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html |
| Alpha Matting |
Closed Form Matting |
A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008. |
http://people.csail.mit.edu/alevin/matting.tar.gz |
| Alpha Matting |
Learning-based Matting |
Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 |
http://www.mathworks.com/matlabcentral/fileexchange/31412 |
| Camera Calibration |
Camera Calibration Toolbox for Matlab |
http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html |
http://www.vision.caltech.edu/bouguetj/calib_doc/ |
| Camera Calibration |
EasyCamCalib |
J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009 |
http://arthronav.isr.uc.pt/easycamcalib/ |
| Camera Calibration |
Epipolar Geometry Toolbox |
G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005 |
http://egt.dii.unisi.it/ |
| Clustering |
Spectral Clustering - UW Project |
|
http://www.stat.washington.edu/spectral/ |
| Clustering |
Spectral Clustering - UCSD Project |
|
http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz |
| Clustering |
Self-Tuning Spectral Clustering |
|
http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html |
| Clustering |
K-Means - Oxford Code |
|
http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip |
| Clustering |
K-Means - VLFeat |
|
http://www.vlfeat.org/ |
| Common Visual Pattern Discovery |
Sketching the Common |
S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010 |
http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz |
| Common Visual Pattern Discovery |
Common Visual Pattern Discovery via Spatially Coherent Correspondences |
H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010 |
https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0 |
| Density Estimation |
Kernel Density Estimation Toolbox |
|
http://www.ics.uci.edu/~ihler/code/kde.html |
| Depth Sensor |
Kinect SDK |
http://www.microsoft.com/en-us/kinectforwindows/ |
http://www.microsoft.com/en-us/kinectforwindows/ |
| Dimension Reduction |
ISOMAP |
|
http://isomap.stanford.edu/ |
| Dimension Reduction |
LLE |
|
http://www.cs.nyu.edu/~roweis/lle/code.html |
| Dimension Reduction |
Laplacian Eigenmaps |
|
http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar |
| Dimension Reduction |
Diffusion maps |
|
http://www.stat.cmu.edu/~annlee/software.htm |
| Dimension Reduction |
Dimensionality Reduction Toolbox |
|
http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html |
| Distance Metric Learning |
Matlab Toolkit for Distance Metric Learning |
|
http://www.cs.cmu.edu/~liuy/distlearn.htm |
| Distance Transformation |
Distance Transforms of Sampled Functions |
|
http://people.cs.uchicago.edu/~pff/dt/ |
| Feature Detection |
Canny Edge Detection |
J. Canny, A Computational Approach To Edge Detection, PAMI, 1986 |
http://www.mathworks.com/help/toolbox/images/ref/edge.html |
| Feature Detection |
FAST Corner Detection |
E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006 |
http://www.edwardrosten.com/work/fast.html |
| Feature Detection |
Edge Foci Interest Points |
L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011 |
http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm |
| Feature Detection |
Boundary Preserving Dense Local Regions |
J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011 |
http://vision.cs.utexas.edu/projects/bplr/bplr.html |
| Feature Extraction |
BRIEF: Binary Robust Independent Elementary Features |
M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010 |
http://cvlab.epfl.ch/research/detect/brief/ |
| Feature Detection andFeature Extraction |
Scale-invariant feature transform (SIFT) - VLFeat |
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. |
http://www.vlfeat.org/ |
| Feature Detection andFeature Extraction |
Scale-invariant feature transform (SIFT) - Demo Software |
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. |
http://www.cs.ubc.ca/~lowe/keypoints/ |
| Feature Extraction |
Global and Efficient Self-Similarity |
T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010and T. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010 |
http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz |
| Feature Detection andFeature Extraction |
Affine-SIFT |
J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009 |
http://www.ipol.im/pub/algo/my_affine_sift/ |
| Feature Detection andFeature Extraction |
Geometric Blur |
A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005 |
http://www.robots.ox.ac.uk/~vgg/software/MKL/ |
| Feature Extraction |
PCA-SIFT |
Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004 |
http://www.cs.cmu.edu/~yke/pcasift/ |
| Feature Detection andFeature Extraction |
Scale-invariant feature transform (SIFT) - Library |
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. |
http://blogs.oregonstate.edu/hess/code/sift/ |
| Feature Detection andFeature Extraction |
Groups of Adjacent Contour Segments |
V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 |
http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz |
| Feature Detection andFeature Extraction |
Speeded Up Robust Feature (SURF) - Matlab Wrapper |
H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 |
http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php |
| Feature Extraction |
Shape Context |
S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002 |
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html |
| Feature Detection andFeature Extraction |
Speeded Up Robust Feature (SURF) - Open SURF |
H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 |
http://www.chrisevansdev.com/computer-vision-opensurf.html |
| Feature Detection andFeature Extraction |
Maximally stable extremal regions (MSER) |
J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 |
http://www.robots.ox.ac.uk/~vgg/research/affine/ |
| Feature Extraction |
GIST Descriptor |
A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001 |
http://people.csail.mit.edu/torralba/code/spatialenvelope/ |
| Feature Detection andFeature Extraction |
Color Descriptor |
K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010 |
http://koen.me/research/colordescriptors/ |
| Feature Extraction |
Local Self-Similarity Descriptor |
E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007 |
http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/ |
| Feature Detection andFeature Extraction |
Maximally stable extremal regions (MSER) - VLFeat |
J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 |
http://www.vlfeat.org/ |
| Feature Extraction |
Pyramids of Histograms of Oriented Gradients (PHOG) |
A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007 |
http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip |
| Feature Detection andFeature Extraction |
Affine Covariant Features |
T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008 |
http://www.robots.ox.ac.uk/~vgg/research/affine/ |
| Feature Extraction |
sRD-SIFT |
M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010 |
http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html# |
| Graph Matching |
Reweighted Random Walks for Graph Matching |
M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010 |
http://cv.snu.ac.kr/research/~RRWM/ |
| Graph Matching |
Hyper-graph Matching via Reweighted Random Walks |
J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011 |
http://cv.snu.ac.kr/research/~RRWHM/ |
| Illumination, Reflectance, and Shadow |
Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences |
J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009 |
http://www.cs.cmu.edu/~jlalonde/software.html#skyModel |
| Illumination, Reflectance, and Shadow |
Ground shadow detection |
J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 |
http://www.jflalonde.org/software.html#shadowDetection |
| Illumination, Reflectance, and Shadow |
Shadow Detection using Paired Region |
R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 |
http://www.cs.illinois.edu/homes/guo29/projects/shadow.html |
| Illumination, Reflectance, and Shadow |
Real-time Specular Highlight Removal |
Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010 |
http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip |
| Illumination, Reflectance, and Shadow |
Estimating Natural Illumination from a Single Outdoor Image |
J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009 |
http://www.cs.cmu.edu/~jlalonde/software.html#skyModel |
| Illumination, Reflectance, and Shadow |
What Does the Sky Tell Us About the Camera? |
J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008 |
http://www.cs.cmu.edu/~jlalonde/software.html#skyModel |
| Image Classification |
Locality-constrained Linear Coding |
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 |
http://www.ifp.illinois.edu/~jyang29/LLC.htm |
| Image Classification |
Sparse Coding for Image Classification |
J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 |
http://www.ifp.illinois.edu/~jyang29/ScSPM.htm |
| Image Classification |
Texture Classification |
M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005 |
http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html |
| Feature Matching andImage Classification |
The Pyramid Match: Efficient Matching for Retrieval and Recognition |
K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005 |
http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm |
| Image Classification |
Spatial Pyramid Matching |
S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 |
http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip |
| Image Deblurring |
Radon Transform |
T. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011 |
http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip |
| Image Deblurring |
Analyzing spatially varying blur |
A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010 |
http://www.eecs.harvard.edu/~ayanc/svblur/ |
| Image Denoising,Image Super-resolution, and Image Deblurring |
Learning Models of Natural Image Patches |
D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011 |
http://www.cs.huji.ac.il/~daniez/ |
| Image Deblurring |
Non-blind deblurring (and blind denoising) with integrated noise estimation |
U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011 |
http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm |
| Image Deblurring |
Eficient Marginal Likelihood Optimization in Blind Deconvolution |
A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011 |
http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip |
| Image Deblurring |
Richardson-Lucy Deblurring for Scenes under Projective Motion Path |
Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011 |
http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip |
| Image Denoising |
Sparsity-based Image Denoising |
W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011 |
http://www.csee.wvu.edu/~xinl/CSR.html |
| Image Denoising |
K-SVD |
|
http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip |
| Image Denoising |
Clustering-based Denoising |
P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009 |
http://users.soe.ucsc.edu/~priyam/K-LLD/ |
| Image Denoising |
BLS-GSM |
|
http://decsai.ugr.es/~javier/denoise/ |
| Image Denoising |
Field of Experts |
|
http://www.cs.brown.edu/~roth/research/software.html |
| Image Denoising |
Non-local Means |
|
http://dmi.uib.es/~abuades/codis/NLmeansfilter.m |
| Image Denoising |
What makes a good model of natural images ? |
Y. Weiss and W. T. Freeman, CVPR 2007 |
http://www.cs.huji.ac.il/~yweiss/BRFOE.zip |
| Image Denoising |
BM3D |
|
http://www.cs.tut.fi/~foi/GCF-BM3D/ |
| Image Denoising |
Kernel Regressions |
|
http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip |
| Image Denoising |
Gaussian Field of Experts |
|
http://www.cs.huji.ac.il/~yweiss/BRFOE.zip |
| Image Denoising |
Nonlocal means with cluster trees |
T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008 |
http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip |
| Image Filtering |
GradientShop |
P. Bhat, C.L. Zitnick, M. Cohen, B. Curless, and J. Kim, GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering, TOG 2010 |
http://grail.cs.washington.edu/projects/gradientshop/ |
| Image Filtering |
Weighted Least Squares Filter |
Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008 |
http://www.cs.huji.ac.il/~danix/epd/ |
| Image Filtering |
Real-time O(1) Bilateral Filtering |
Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009 |
http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip |
| Image Filtering |
Guided Image Filtering |
K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010 |
http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar |
| Image Filtering |
Fast Bilateral Filter |
S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006 |
http://people.csail.mit.edu/sparis/bf/ |
| Image Filtering |
Image smoothing via L0 Gradient Minimization |
L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011 |
http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip |
| Image Filtering |
Domain Transformation |
E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011 |
http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip |
| Image Processing andImage Filtering |
Piotr‘s Image & Video Matlab Toolbox |
Piotr Dollar, Piotr‘s Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html |
http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html |
| Image Filtering |
Local Laplacian Filters |
S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011 |
http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip |
| Image Filtering |
SVM for Edge-Preserving Filtering |
Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010 |
http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip |
| Image Filtering |
Anisotropic Diffusion |
P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990 |
http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik |
| Image Quality Assessment |
SPIQA |
|
http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip |
| Image Quality Assessment |
Degradation Model |
|
http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html |
| Image Quality Assessment |
Feature SIMilarity Index |
|
http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm |
| Image Quality Assessment |
Structural SIMilarity |
|
https://ece.uwaterloo.ca/~z70wang/research/ssim/ |
| Image Segmentation |
Segmentation by Minimum Code Length |
A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007 |
http://perception.csl.uiuc.edu/coding/image_segmentation/ |
| Image Segmentation |
Normalized Cut |
J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 |
http://www.cis.upenn.edu/~jshi/software/ |
| Image Segmentation |
Entropy Rate Superpixel Segmentation |
M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 |
http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip |
| Image Segmentation |
Mean-Shift Image Segmentation - EDISON |
D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 |
http://coewww.rutgers.edu/riul/research/code/EDISON/index.html |
| Image Segmentation |
Efficient Graph-based Image Segmentation - Matlab Wrapper |
P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 |
http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation |
| Image Segmentation |
Biased Normalized Cut |
S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 |
http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/ |
| Image Segmentation |
Multiscale Segmentation Tree |
E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 and N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 |
http://vision.ai.uiuc.edu/segmentation |
| Image Segmentation |
Efficient Graph-based Image Segmentation - C++ code |
P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 |
http://people.cs.uchicago.edu/~pff/segment/ |
| Image Segmentation |
Superpixel by Gerg Mori |
X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 |
http://www.cs.sfu.ca/~mori/research/superpixels/ |
| Image Segmentation |
Segmenting Scenes by Matching Image Composites |
B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009 |
http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html |
| Image Segmentation |
Recovering Occlusion Boundaries from a Single Image |
D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. |
http://www.cs.cmu.edu/~dhoiem/software/ |
| Image Segmentation |
Quick-Shift |
A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 |
http://www.vlfeat.org/overview/quickshift.html |
| Image Segmentation |
SLIC Superpixels |
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 |
http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html |
| Image Segmentation |
Mean-Shift Image Segmentation - Matlab Wrapper |
D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 |
http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz |
| Image Segmentation |
OWT-UCM Hierarchical Segmentation |
P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011 |
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html |
| Image Segmentation |
Turbepixels |
A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 |
http://www.cs.toronto.edu/~babalex/research.html |
| Image Super-resolution |
MRF for image super-resolution |
W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 |
http://people.csail.mit.edu/billf/project pages/sresCode/Markov Random Fields for Super-Resolution.html |
| Image Super-resolution |
Single-Image Super-Resolution Matlab Package |
R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010 |
http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip |
| Image Super-resolution |
Self-Similarities for Single Frame Super-Resolution |
C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 |
https://eng.ucmerced.edu/people/cyang35/ACCV10.zip |
| Image Super-resolution |
MDSP Resolution Enhancement Software |
S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004 |
http://users.soe.ucsc.edu/~milanfar/software/superresolution.html |
| Image Super-resolution |
Sprarse coding super-resolution |
J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 |
http://www.ifp.illinois.edu/~jyang29/ScSR.htm |
| Image Super-resolution |
Multi-frame image super-resolution |
Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis |
http://www.robots.ox.ac.uk/~vgg/software/SR/index.html |
| Image Understanding |
SuperParsing |
J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010 |
http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip |
| Image Understanding |
Discriminative Models for Multi-Class Object Layout |
C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011 |
http://www.ics.uci.edu/~desaic/multiobject_context.zip |
| Image Understanding |
Nonparametric Scene Parsing via Label Transfer |
C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 |
http://people.csail.mit.edu/celiu/LabelTransfer/index.html |
| Image Understanding |
Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics |
A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010 |
http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads |
| Image Understanding |
Towards Total Scene Understanding |
L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 |
http://vision.stanford.edu/projects/totalscene/index.html |
| Image Understanding |
Object Bank |
Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010 |
http://vision.stanford.edu/projects/objectbank/index.html |
| Kernels and Distances |
Fast Directional Chamfer Matching |
|
http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip |
| Kernels and Distances |
Efficient Earth Mover‘s Distance with L1 Ground Distance (EMD_L1) |
H. Ling and K. Okada, An Efficient Earth Mover‘s Distance Algorithm for Robust Histogram Comparison, PAMI 2007 |
http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip |
| Kernels and Distances |
Diffusion-based distance |
H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 |
http://www.dabi.temple.edu/~hbling/code/DD_v1.zip |
| Low-Rank Modeling |
TILT: Transform Invariant Low-rank Textures |
Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 |
http://perception.csl.uiuc.edu/matrix-rank/tilt.html |
| Low-Rank Modeling |
Low-Rank Matrix Recovery and Completion |
|
http://perception.csl.uiuc.edu/matrix-rank/sample_code.html |
| Low-Rank Modeling |
RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition |
Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010 |
http://perception.csl.uiuc.edu/matrix-rank/rasl.html |
| MRF Optimization |
MRF Minimization Evaluation |
R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 |
http://vision.middlebury.edu/MRF/ |
| MRF Optimization |
Max-flow/min-cut for shape fitting |
V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007 |
http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip |
| MRF Optimization |
Max-flow/min-cut |
Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 |
http://vision.csd.uwo.ca/code/maxflow-v3.01.zip |
| MRF Optimization |
Planar Graph Cut |
F. R. Schmidt, E. Toppe and D. Cremers, Ef?cient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 |
http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip |
| MRF Optimization |
Max-flow/min-cut for massive grids |
A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008 |
http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip |
| MRF Optimization |
Multi-label optimization |
Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 |
http://vision.csd.uwo.ca/code/gco-v3.0.zip |
| Machine Learning |
Statistical Pattern Recognition Toolbox |
M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 |
http://cmp.felk.cvut.cz/cmp/software/stprtool/ |
| Machine Learning |
Netlab Neural Network Software |
C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995 |
http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/ |
| Machine Learning |
Boosting Resources by Liangliang Cao |
http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm |
http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm |
| Machine Learning |
FastICA package for MATLAB |
http://research.ics.tkk.fi/ica/book/ |
http://research.ics.tkk.fi/ica/fastica/ |
| Multi-View Stereo |
Patch-based Multi-view Stereo Software |
Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 |
http://grail.cs.washington.edu/software/pmvs/ |
| Multi-View Stereo |
Clustering Views for Multi-view Stereo |
Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 |
http://grail.cs.washington.edu/software/cmvs/ |
| Multi-View Stereo |
Multi-View Stereo Evaluation |
S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 |
http://vision.middlebury.edu/mview/ |
| Multiple Instance Learning |
DD-SVM |
Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004 |
|
| Multiple Instance Learning |
MIForests |
C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 |
http://www.ymer.org/amir/software/milforests/ |
| Multiple Instance Learning |
MILIS |
Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010 |
|
| Multiple Instance Learning |
MILES |
Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 |
http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/ |
| Multiple Kernel Learning |
SHOGUN |
S. Sonnenburg, G. R?tsch, C. Sch?fer, B. Sch?lkopf . Large scale multiple kernel learning. JMLR, 2006 |
http://www.shogun-toolbox.org/ |
| Multiple Kernel Learning |
OpenKernel.org |
F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011 |
http://www.openkernel.org/ |
| Multiple Kernel Learning |
SimpleMKL |
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008 |
http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html |
| Multiple Kernel Learning |
DOGMA |
F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 |
http://dogma.sourceforge.net/ |
| Multiple View Geometry |
MATLAB and Octave Functions for Computer Vision and Image Processing |
P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns |
http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html |
| Multiple View Geometry |
Matlab Functions for Multiple View Geometry |
|
http://www.robots.ox.ac.uk/~vgg/hzbook/code/ |
| Nearest Neighbors Matching |
ANN: Approximate Nearest Neighbor Searching |
|
http://www.cs.umd.edu/~mount/ANN/ |
| Nearest Neighbors Matching |
Spectral Hashing |
Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 |
http://www.cs.huji.ac.il/~yweiss/SpectralHashing/ |
| Nearest Neighbors Matching |
Coherency Sensitive Hashing |
S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011 |
http://www.eng.tau.ac.il/~simonk/CSH/index.html |
| Nearest Neighbors Matching |
FLANN: Fast Library for Approximate Nearest Neighbors |
|
http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN |
| Nearest Neighbors Matching |
LDAHash: Binary Descriptors for Matching in Large Image Databases |
C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011. |
http://cvlab.epfl.ch/research/detect/ldahash/index.php |
| Object Detection |
Poselet |
L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 |
http://www.eecs.berkeley.edu/~lbourdev/poselets/ |
| Object Detection |
Cascade Object Detection with Deformable Part Models |
P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 |
http://people.cs.uchicago.edu/~rbg/star-cascade/ |
| Object Detection |
Multiple Kernels |
A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009 |
http://www.robots.ox.ac.uk/~vgg/software/MKL/ |
| Object Detection |
Hough Forests for Object Detection |
J. Gall and V. Lempitsky, Class-Speci?c Hough Forests for Object Detection, CVPR, 2009 |
http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html |
| Object Detection |
Discriminatively Trained Deformable Part Models |
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 |
http://people.cs.uchicago.edu/~pff/latent/ |
| Feature Extraction andObject Detection |
Histogram of Oriented Graidents - OLT for windows |
N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 |
http://www.computing.edu.au/~12482661/hog.html |
| Feature Extraction andObject Detection |
Histogram of Oriented Graidents - INRIA Object Localization Toolkit |
N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 |
http://www.navneetdalal.com/software |
| Object Detection |
Recognition using regions |
C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009 |
http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip |
| Object Detection |
A simple parts and structure object detector |
ICCV 2005 short courses on Recognizing and Learning Object Categories |
http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html |
| Object Detection |
Feature Combination |
P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 |
http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html |
| Object Detection |
Ensemble of Exemplar-SVMs |
T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011 |
http://www.cs.cmu.edu/~tmalisie/projects/iccv11/ |
| Object Detection |
A simple object detector with boosting |
ICCV 2005 short courses on Recognizing and Learning Object Categories |
http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html |
| Object Detection |
Max-Margin Hough Transform |
S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 |
http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/ |
| Object Detection |
Implicit Shape Model |
B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008 |
http://www.vision.ee.ethz.ch/~bleibe/code/ism.html |
| Object Detection |
Ensemble of Exemplar-SVMs for Object Detection and Beyond |
T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011 |
http://www.cs.cmu.edu/~tmalisie/projects/iccv11/ |
| Object Detection |
Viola-Jones Object Detection |
P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001 |
http://pr.willowgarage.com/wiki/FaceDetection |
| Object Discovery |
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections |
B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006 |
http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html |
| Object Proposal |
Objectness measure |
B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 |
http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz |
| Object Proposal |
Parametric min-cut |
J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 |
http://sminchisescu.ins.uni-bonn.de/code/cpmc/ |
| Object Proposal |
Region-based Object Proposal |
I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010 |
http://vision.cs.uiuc.edu/proposals/ |
| Object Recognition |
Recognition by Association via Learning Per-exemplar Distances |
T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008 |
http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz |
| Object Recognition |
Biologically motivated object recognition |
T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 |
http://cbcl.mit.edu/software-datasets/standardmodel/index.html |
| Object Segmentation |
Geodesic Star Convexity for Interactive Image Segmentation |
V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation |
http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml |
| Object Segmentation |
ClassCut for Unsupervised Class Segmentation |
B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010 |
http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip |
| Object Segmentation |
Sparse to Dense Labeling |
P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 |
http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz |
| Optical Flow |
Optical Flow by Deqing Sun |
D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 |
http://www.cs.brown.edu/~dqsun/code/flow_code.zip |
| Optical Flow |
Classical Variational Optical Flow |
T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 |
http://lmb.informatik.uni-freiburg.de/resources/binaries/ |
| Optical Flow |
Large Displacement Optical Flow |
T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 |
http://lmb.informatik.uni-freiburg.de/resources/binaries/ |
| Optical Flow |
Dense Point Tracking |
N. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010 |
http://lmb.informatik.uni-freiburg.de/resources/binaries/ |
| Optical Flow |
Optical Flow Evaluation |
S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 |
http://vision.middlebury.edu/flow/ |
| Optical Flow |
Horn and Schunck‘s Optical Flow |
|
http://www.cs.brown.edu/~dqsun/code/hs.zip |
| Optical Flow |
Black and Anandan‘s Optical Flow |
|
http://www.cs.brown.edu/~dqsun/code/ba.zip |
| Pose Estimation |
Training Deformable Models for Localization |
Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006 |
http://www.ics.uci.edu/~dramanan/papers/parse/index.html |
| Pose Estimation |
Calvin Upper-Body Detector |
E. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009 |
http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/ |
| Pose Estimation |
Articulated Pose Estimation using Flexible Mixtures of Parts |
Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011 |
http://phoenix.ics.uci.edu/software/pose/ |
| Pose Estimation |
Estimating Human Pose from Occluded Images |
J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009 |
http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip |
| Saliency Detection |
Saliency detection: A spectral residual approach |
X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007 |
http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html |
| Saliency Detection |
Saliency Using Natural statistics |
L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008 |
http://cseweb.ucsd.edu/~l6zhang/ |
| Saliency Detection |
Attention via Information Maximization |
N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005 |
http://www.cse.yorku.ca/~neil/AIM.zip |
| Saliency Detection |
Itti, Koch, and Niebur‘ saliency detection |
L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998 |
http://www.saliencytoolbox.net/ |
| Saliency Detection |
Frequency-tuned salient region detection |
R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009 |
http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html |
| Saliency Detection |
Saliency-based video segmentation |
K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009 |
http://www.brl.ntt.co.jp/people/akisato/saliency3.html |
| Saliency Detection |
Segmenting salient objects from images and videos |
E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010 |
http://www.cse.oulu.fi/MVG/Downloads/saliency |
| Saliency Detection |
Graph-based visual saliency |
J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007 |
http://www.klab.caltech.edu/~harel/share/gbvs.php |
| Saliency Detection |
Learning to Predict Where Humans Look |
T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009 |
http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html |
| Saliency Detection |
Spectrum Scale Space based Visual Saliency |
J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011 |
http://www.cim.mcgill.ca/~lijian/saliency.htm |
| Saliency Detection |
Discriminant Saliency for Visual Recognition from Cluttered Scenes |
D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004 |
http://www.svcl.ucsd.edu/projects/saliency/ |
| Saliency Detection |
Context-aware saliency detection |
S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. |
http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html |
| Saliency Detection |
Saliency detection using maximum symmetric surround |
R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010 |
http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html |
| Saliency Detection |
Global Contrast based Salient Region Detection |
M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011 |
http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/ |
| Saliency Detection |
Learning Hierarchical Image Representation with Sparsity, Saliency and Locality |
J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011 |
|
| Sparse Representation |
Centralized Sparse Representation for Image Restoration |
W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011 |
http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip |
| Sparse Representation |
Efficient sparse coding algorithms |
H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007 |
http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm |
| Sparse Representation |
Fisher Discrimination Dictionary Learning for Sparse Representation |
M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011 |
http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip |
| Sparse Representation |
Robust Sparse Coding for Face Recognition |
M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011 |
http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip |
| Sparse Representation |
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing |
M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing |
http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar |
| Sparse Representation |
SPArse Modeling Software |
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010 |
http://www.di.ens.fr/willow/SPAMS/ |
| Sparse Representation |
Sparse coding simulation software |
Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996 |
http://redwood.berkeley.edu/bruno/sparsenet/ |
| Sparse Representation |
A Linear Subspace Learning Approach via Sparse Coding |
L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011 |
http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip |
| Stereo |
Constant-Space Belief Propagation |
Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010 |
http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm |
| Stereo |
Stereo Evaluation |
D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001 |
http://vision.middlebury.edu/stereo/ |
| Image Denoising andStereo Matching |
Efficient Belief Propagation for Early Vision |
P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006 |
http://www.cs.brown.edu/~pff/bp/ |
| Structure from motion |
Nonrigid Structure From Motion in Trajectory Space |
|
http://cvlab.lums.edu.pk/nrsfm/index.html |
| Structure from motion |
libmv |
|
http://code.google.com/p/libmv/ |
| Structure from motion |
Bundler |
N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006 |
http://phototour.cs.washington.edu/bundler/ |
| Structure from motion |
FIT3D |
|
http://www.fit3d.info/ |
| Structure from motion |
VisualSFM : A Visual Structure from Motion System |
|
http://www.cs.washington.edu/homes/ccwu/vsfm/ |
| Structure from motion |
OpenSourcePhotogrammetry |
|
http://opensourcephotogrammetry.blogspot.com/ |
| Structure from motion |
Structure and Motion Toolkit in Matlab |
|
http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm |
| Structure from motion |
Structure from Motion toolbox for Matlab by Vincent Rabaud |
|
http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/ |
| Subspace Learning |
Generalized Principal Component Analysis |
R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003 |
http://www.vision.jhu.edu/downloads/main.php?dlID=c1 |
| Text Recognition |
Text recognition in the wild |
K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011 |
http://vision.ucsd.edu/~kai/grocr/ |
| Text Recognition |
Neocognitron for handwritten digit recognition |
K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003 |
http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375 |
| Texture Synthesis |
Image Quilting for Texture Synthesis and Transfer |
A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001 |
http://www.cs.cmu.edu/~efros/quilt_research_code.zip |
| Visual Tracking |
GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker |
S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007 |
http://cs.unc.edu/~ssinha/Research/GPU_KLT/ |
| Visual Tracking |
Superpixel Tracking |
S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011 |
http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html |
| Visual Tracking |
Tracking with Online Multiple Instance Learning |
B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011 |
http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml |
| Visual Tracking |
Motion Tracking in Image Sequences |
C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000 |
http://www.cs.berkeley.edu/~flw/tracker/ |
| Visual Tracking |
L1 Tracking |
X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009 |
http://www.dabi.temple.edu/~hbling/code_data.htm |
| Visual Tracking |
Online Discriminative Object Tracking with Local Sparse Representation |
Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012 |
http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip |
| Visual Tracking |
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker |
B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981 |
http://www.ces.clemson.edu/~stb/klt/ |
| Visual Tracking |
Online boosting trackers |
H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006 |
http://www.vision.ee.ethz.ch/boostingTrackers/ |
| Visual Tracking |
Visual Tracking Decomposition |
J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010 |
http://cv.snu.ac.kr/research/~vtd/ |
| Visual Tracking |
Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects |
H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011 |
http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz |
| Visual Tracking |
Lucas-Kanade affine template tracking |
S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002 |
http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking |
| Visual Tracking |
Object Tracking |
A. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006 |
http://plaza.ufl.edu/lvtaoran/object tracking.htm |
| Visual Tracking |
Visual Tracking with Histograms and Articulating Blocks |
S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008 |
http://www.cise.ufl.edu/~smshahed/tracking.htm |
| Visual Tracking |
Tracking using Pixel-Wise Posteriors |
C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008 |
http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml |
| Visual Tracking |
Incremental Learning for Robust Visual Tracking |
D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 |
http://www.cs.toronto.edu/~dross/ivt/ |
| Visual Tracking |
Particle Filter Object Tracking |
|
http://blogs.oregonstate.edu/hess/code/particles/ |
