UIUC同学Jia-Bin Huang收集的计算机视觉代码合集
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UIUC同学Jia-Bin Huang收集的计算机视觉代码合集
Topic
Name
Reference
Link
Feature Detection,Feature Extraction, and
Space-Time Interest Points (STIP)
I. Laptev, On Space-Time Interest Points, IJCV, 2005
http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip
Action Recognition
3D Gradients (HOG3D)
A. Klaser, M. Marszałek, 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
Scale-invariant feature transform (SIFT) - VLFeat
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
http://www.vlfeat.org/
Feature Detection
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
http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz
Feature Detection
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
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
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
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
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
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
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
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
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
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
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
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/KernelRegressionBasedIma
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
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
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, Efficient 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. Rätsch, C. Schäfer, B. Schölkopf . 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-Specific 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
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
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
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
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/
Other useful links (dataset, lectures, and other softwares)
Conference Information
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