3D Computer Vision: Past, Present, and Future
|
Talk
|
3D Computer Vision
|
http://www.youtube.com/watch?v=kyIzMr917Rc
|
Steven Seitz, University of Washington, Google Tech Talk, 2011
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Computer Vision and 3D Perception for Robotics
|
Tutorial
|
3D perception
|
http://www.willowgarage.com/workshops/2010/eccv
|
Radu Bogdan Rusu, Gary Bradski, Caroline Pantofaru, Stefan Hinterstoisser, Stefan Holzer, Kurt Konolige and Andrea Vedaldi, ECCV 2010 Tutorial
|
|
3D point cloud processing: PCL (Point Cloud Library)
|
Tutorial
|
3D point cloud processing
|
http://www.pointclouds.org/media/iccv2011.html
|
R. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorial
|
|
Looking at people: The past, the present and the future
|
Tutorial
|
Action Recognition
|
http://www.cs.brown.edu/~ls/iccv2011tutorial.html
|
L. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorial
|
|
Frontiers of Human Activity Analysis
|
Tutorial
|
Action Recognition
|
http://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/
|
J. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorial
|
|
Statistical and Structural Recognition of Human Actions
|
Tutorial
|
Action Recognition
|
https://sites.google.com/site/humanactionstutorialeccv10/
|
Ivan Laptev and Greg Mori, ECCV 2010 Tutorial
|
|
Dense Trajectories Video Description
|
Code
|
Action Recognition
|
http://lear.inrialpes.fr/people/wang/dense_trajectories
|
H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011
|
|
3D Gradients (HOG3D)
|
Code
|
Action Recognition
|
http://lear.inrialpes.fr/people/klaeser/research_hog3d
|
A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008.
|
|
Spectral Matting
|
Code
|
Alpha Matting
|
http://www.vision.huji.ac.il/SpectralMatting/
|
A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008
|
|
Learning-based Matting
|
Code
|
Alpha Matting
|
http://www.mathworks.com/matlabcentral/fileexchange/31412
|
Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009
|
|
Bayesian Matting
|
Code
|
Alpha Matting
|
http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html
|
Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001
|
|
Closed Form Matting
|
Code
|
Alpha Matting
|
http://people.csail.mit.edu/alevin/matting.tar.gz
|
A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008.
|
|
Shared Matting
|
Code
|
Alpha Matting
|
http://www.inf.ufrgs.br/~eslgastal/SharedMatting/
|
E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010
|
|
Introduction To Bayesian Inference
|
Talk
|
Bayesian Inference
|
http://videolectures.net/mlss09uk_bishop_ibi/
|
Christopher Bishop, Microsoft Research
|
|
Modern Bayesian Nonparametrics
|
Talk
|
Bayesian Nonparametrics
|
http://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfu
|
Peter Orbanz and Yee Whye Teh
|
|
Theory and Applications of Boosting
|
Talk
|
Boosting
|
http://videolectures.net/mlss09us_schapire_tab/
|
Robert Schapire, Department of Computer Science, Princeton University
|
|
Epipolar Geometry Toolbox
|
Code
|
Camera Calibration
|
http://egt.dii.unisi.it/
|
G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005
|
|
Camera Calibration Toolbox for Matlab
|
Code
|
Camera Calibration
|
http://www.vision.caltech.edu/bouguetj/calib_doc/
|
http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html
|
|
EasyCamCalib
|
Code
|
Camera Calibration
|
http://arthronav.isr.uc.pt/easycamcalib/
|
J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009
|
|
Spectral Clustering - UCSD Project
|
Code
|
Clustering
|
http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz
|
|
K-Means - Oxford Code
|
Code
|
Clustering
|
http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip
|
|
Self-Tuning Spectral Clustering
|
Code
|
Clustering
|
http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html
|
|
K-Means - VLFeat
|
Code
|
Clustering
|
http://www.vlfeat.org/
|
|
Spectral Clustering - UW Project
|
Code
|
Clustering
|
http://www.stat.washington.edu/spectral/
|
|
Color image understanding: from acquisition to high-level image understanding
|
Tutorial
|
Color Image Processing
|
http://www.cat.uab.cat/~joost/tutorial_iccv.html
|
Theo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorial
|
|
Sketching the Common
|
Code
|
Common Visual Pattern Discovery
|
http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz
|
S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010
|
|
Common Visual Pattern Discovery via Spatially Coherent Correspondences
|
Code
|
Common Visual Pattern Discovery
|
https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0
|
H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010
|
|
Fcam: an architecture and API for computational cameras
|
Tutorial
|
Computational Imaging
|
http://fcam.garage.maemo.org/iccv2011.html
|
Kari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorial
|
|
Computational Photography, University of Illinois, Urbana-Champaign, Fall 2011
|
Course
|
Computational Photography
|
http://www.cs.illinois.edu/class/fa11/cs498dh/
|
Derek Hoiem
|
|
Computational Photography, CMU, Fall 2011
|
Course
|
Computational Photography
|
http://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.html
|
Alexei “Alyosha” Efros
|
|
Computational Symmetry: Past, Current, Future
|
Tutorial
|
Computational Symmetry
|
http://vision.cse.psu.edu/research/symmComp/index.shtml
|
Yanxi Liu, ECCV 2010 Tutorial
|
|
Introduction to Computer Vision, Stanford University, Winter 2010-2011
|
Course
|
Computer Vision
|
http://vision.stanford.edu/teaching/cs223b/
|
Fei-Fei Li
|
|
Computer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012
|
Course
|
Computer Vision
|
https://www.coursera.org/course/computervision
|
Silvio Savarese and Fei-Fei Li
|
|
Computer Vision, University of Texas at Austin, Spring 2011
|
Course
|
Computer Vision
|
http://www.cs.utexas.edu/~grauman/courses/spring2011/index.html
|
Kristen Grauman
|
|
Learning-Based Methods in Vision, CMU, Spring 2012
|
Course
|
Computer Vision
|
https://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0
|
Alexei “Alyosha” Efros and Leonid Sigal
|
|
Introduction to Computer Vision
|
Course
|
Computer Vision
|
http://www.cs.brown.edu/courses/cs143/
|
James Hays, Brown University, Fall 2011
|
|
Computer Image Analysis, Computer Vision Conferences
|
Link
|
Computer Vision
|
http://iris.usc.edu/information/Iris-Conferences.html
|
USC
|
|
CV Papers on the web
|
Link
|
Computer Vision
|
http://www.cvpapers.com/index.html
|
CVPapers
|
|
Computer Vision, University of North Carolina at Chapel Hill, Spring 2010
|
Course
|
Computer Vision
|
http://www.cs.unc.edu/~lazebnik/spring10/
|
Svetlana Lazebnik
|
|
CVonline
|
Link
|
Computer Vision
|
http://homepages.inf.ed.ac.uk/rbf/CVonline/
|
CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
|
|
Computer Vision: The Fundamentals, University of California at Berkeley, Fall 2012
|
Course
|
Computer Vision
|
https://www.coursera.org/course/vision
|
Jitendra Malik
|
|
Computer Vision, New York University, Fall 2012
|
Course
|
Computer Vision
|
http://cs.nyu.edu/~fergus/teaching/vision_2012/index.html
|
Rob Fergus
|
|
Advances in Computer Vision
|
Course
|
Computer Vision
|
http://groups.csail.mit.edu/vision/courses/6.869/
|
Antonio Torralba, MIT, Spring 2010
|
|
Annotated Computer Vision Bibliography
|
Link
|
Computer Vision
|
http://iris.usc.edu/Vision-Notes/bibliography/contents.html
|
compiled by Keith Price
|
|
Computer Vision, University of Illinois, Urbana-Champaign, Spring 2012
|
Course
|
Computer Vision
|
http://www.cs.illinois.edu/class/sp12/cs543/
|
Derek Hoiem
|
|
The Computer Vision homepage
|
Link
|
Computer Vision
|
http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
|
|
Computer Vision, University of Washington, Winter 2012
|
Course
|
Computer Vision
|
http://www.cs.washington.edu/education/courses/cse455/12wi/
|
Steven Seitz
|
|
CV Datasets on the web
|
Link
|
Computer Vision
|
http://www.cvpapers.com/datasets.html
|
CVPapers
|
|
The Computer Vision Industry
|
Link
|
Computer Vision Industry
|
http://www.cs.ubc.ca/~lowe/vision.html
|
David Lowe
|
|
Compiled list of recognition datasets
|
Link
|
Dataset
|
http://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htm
|
compiled by Kristen Grauman
|
|
Decision forests for classification, regression, clustering and density estimation
|
Tutorial
|
Decision Forests
|
http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx
|
A. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorial
|
|
A tutorial on Deep Learning
|
Talk
|
Deep Learning
|
http://videolectures.net/jul09_hinton_deeplearn/
|
Geoffrey E. Hinton, Department of Computer Science, University of Toronto
|
|
Kernel Density Estimation Toolbox
|
Code
|
Density Estimation
|
http://www.ics.uci.edu/~ihler/code/kde.html
|
|
Kinect SDK
|
Code
|
Depth Sensor
|
http://www.microsoft.com/en-us/kinectforwindows/
|
http://www.microsoft.com/en-us/kinectforwindows/
|
|
LLE
|
Code
|
Dimension Reduction
|
http://www.cs.nyu.edu/~roweis/lle/code.html
|
|
Laplacian Eigenmaps
|
Code
|
Dimension Reduction
|
http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar
|
|
Diffusion maps
|
Code
|
Dimension Reduction
|
http://www.stat.cmu.edu/~annlee/software.htm
|
|
ISOMAP
|
Code
|
Dimension Reduction
|
http://isomap.stanford.edu/
|
|
Dimensionality Reduction Toolbox
|
Code
|
Dimension Reduction
|
http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
|
|
Matlab Toolkit for Distance Metric Learning
|
Code
|
Distance Metric Learning
|
http://www.cs.cmu.edu/~liuy/distlearn.htm
|
|
Distance Functions and Metric Learning
|
Tutorial
|
Distance Metric Learning
|
http://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/
|
M. Werman, O. Pele and B. Kulis, ECCV 2010 Tutorial
|
|
Distance Transforms of Sampled Functions
|
Code
|
Distance Transformation
|
http://people.cs.uchicago.edu/~pff/dt/
|
|
Hidden Markov Models
|
Tutorial
|
Expectation Maximization
|
http://crow.ee.washington.edu/people/bulyko/papers/em.pdf
|
Jeff A. Bilmes, University of California at Berkeley
|
|
Edge Foci Interest Points
|
Code
|
Feature Detection
|
http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm
|
L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011
|
|
Boundary Preserving Dense Local Regions
|
Code
|
Feature Detection
|
http://vision.cs.utexas.edu/projects/bplr/bplr.html
|
J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011
|
|
Canny Edge Detection
|
Code
|
Feature Detection
|
http://www.mathworks.com/help/toolbox/images/ref/edge.html
|
J. Canny, A Computational Approach To Edge Detection, PAMI, 1986
|
|
FAST Corner Detection
|
Code
|
Feature Detection
|
http://www.edwardrosten.com/work/fast.html
|
E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006
|
|
Groups of Adjacent Contour Segments
|
Code
|
Feature Detection; Feature Extraction
|
http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz
|
V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007
|
|
Maximally stable extremal regions (MSER) - VLFeat
|
Code
|
Feature Detection; Feature Extraction
|
http://www.vlfeat.org/
|
J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002
|
|
Geometric Blur
|
Code
|
Feature Detection; Feature Extraction
|
http://www.robots.ox.ac.uk/~vgg/software/MKL/
|
A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005
|
|
Affine-SIFT
|
Code
|
Feature Detection; Feature Extraction
|
http://www.ipol.im/pub/algo/my_affine_sift/
|
J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009
|
|
Scale-invariant feature transform (SIFT) - Demo Software
|
Code
|
Feature Detection; Feature Extraction
|
http://www.cs.ubc.ca/~lowe/keypoints/
|
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
|
|
Affine Covariant Features
|
Code
|
Feature Detection; Feature Extraction
|
http://www.robots.ox.ac.uk/~vgg/research/affine/
|
T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008
|
|
Scale-invariant feature transform (SIFT) - Library
|
Code
|
Feature Detection; Feature Extraction
|
http://blogs.oregonstate.edu/hess/code/sift/
|
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
|
|
Maximally stable extremal regions (MSER)
|
Code
|
Feature Detection; Feature Extraction
|
http://www.robots.ox.ac.uk/~vgg/research/affine/
|
J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002
|
|
Color Descriptor
|
Code
|
Feature Detection; Feature Extraction
|
http://koen.me/research/colordescriptors/
|
K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010
|
|
Speeded Up Robust Feature (SURF) - Open SURF
|
Code
|
Feature Detection; Feature Extraction
|
http://www.chrisevansdev.com/computer-vision-opensurf.html
|
H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006
|
|
Scale-invariant feature transform (SIFT) - VLFeat
|
Code
|
Feature Detection; Feature Extraction
|
http://www.vlfeat.org/
|
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
|
|
Speeded Up Robust Feature (SURF) - Matlab Wrapper
|
Code
|
Feature Detection; Feature Extraction
|
http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php
|
H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006
|
|
Space-Time Interest Points (STIP)
|
Code
|
Feature Detection; Feature Extraction; Action Recognition
|
http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip;http://www.nada.kth.se/cvap/abstracts/cvap284.html
|
I. Laptev, On Space-Time Interest Points, IJCV, 2005; I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005
|
|
PCA-SIFT
|
Code
|
Feature Extraction
|
http://www.cs.cmu.edu/~yke/pcasift/
|
Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004
|
|
sRD-SIFT
|
Code
|
Feature Extraction
|
http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html#
|
M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010
|
|
Local Self-Similarity Descriptor
|
Code
|
Feature Extraction
|
http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/
|
E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007
|
|
Pyramids of Histograms of Oriented Gradients (PHOG)
|
Code
|
Feature Extraction
|
http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip
|
A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007
|
|
BRIEF: Binary Robust Independent Elementary Features
|
Code
|
Feature Extraction
|
http://cvlab.epfl.ch/research/detect/brief/
|
M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010
|
|
Global and Efficient Self-Similarity
|
Code
|
Feature Extraction
|
http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz
|
T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010; T. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010
|
GIST Descriptor
|
Code
|
Feature Extraction
|
http://people.csail.mit.edu/torralba/code/spatialenvelope/
|
A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001
|
|
Shape Context
|
Code
|
Feature Extraction
|
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html
|
S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002
|
|
Image and Video Description with Local Binary Pattern Variants
|
Tutorial
|
Feature Extraction
|
http://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdf
|
M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial
|
|
Histogram of Oriented Graidents - OLT for windows
|
Code
|
Feature Extraction; Object Detection
|
http://www.computing.edu.au/~12482661/hog.html
|
N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005
|
|
Histogram of Oriented Graidents - INRIA Object Localization Toolkit
|
Code
|
Feature Extraction; Object Detection
|
http://www.navneetdalal.com/software
|
N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005
|
|
Feature Learning for Image Classification
|
Tutorial
|
Feature Learning, Image Classification
|
http://ufldl.stanford.edu/eccv10-tutorial/
|
Kai Yu and Andrew Ng, ECCV 2010 Tutorial
|
|
The Pyramid Match: Efficient Matching for Retrieval and Recognition
|
Code
|
Feature Matching; Image Classification
|
http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm
|
K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005
|
|
Game Theory in Computer Vision and Pattern Recognition
|
Tutorial
|
Game Theory
|
http://www.dsi.unive.it/~atorsell/cvpr2011tutorial/
|
Marcello Pelillo and Andrea Torsello, CVPR 2011 Tutorial
|
|
Gaussian Process Basics
|
Talk
|
Gaussian Process
|
http://videolectures.net/gpip06_mackay_gpb/
|
David MacKay, University of Cambridge
|
|
Hyper-graph Matching via Reweighted Random Walks
|
Code
|
Graph Matching
|
http://cv.snu.ac.kr/research/~RRWHM/
|
J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011
|
|
Reweighted Random Walks for Graph Matching
|
Code
|
Graph Matching
|
http://cv.snu.ac.kr/research/~RRWM/
|
M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010
|
|
Learning with inference for discrete graphical models
|
Tutorial
|
Graphical Models
|
http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/
|
Nikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorial
|
|
Graphical Models and message-passing algorithms
|
Talk
|
Graphical Models
|
http://videolectures.net/mlss2011_wainwright_messagepassing/
|
Martin J. Wainwright, University of California at Berkeley
|
|
Graphical Models, Exponential Families, and Variational Inference
|
Tutorial
|
Graphical Models
|
http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf
|
Martin J. Wainwright and Michael I. Jordan, University of California at Berkeley
|
|
Inference in Graphical Models, Stanford University, Spring 2012
|
Course
|
Graphical Models
|
http://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.html
|
Andrea Montanari, Stanford University
|
|
Ground shadow detection
|
Code
|
Illumination, Reflectance, and Shadow
|
http://www.jflalonde.org/software.html#shadowDetection
|
J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010
|
|
Estimating Natural Illumination from a Single Outdoor Image
|
Code
|
Illumination, Reflectance, and Shadow
|
http://www.cs.cmu.edu/~jlalonde/software.html#skyModel
|
J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009
|
|
What Does the Sky Tell Us About the Camera?
|
Code
|
Illumination, Reflectance, and Shadow
|
http://www.cs.cmu.edu/~jlalonde/software.html#skyModel
|
J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008
|
|
Shadow Detection using Paired Region
|
Code
|
Illumination, Reflectance, and Shadow
|
http://www.cs.illinois.edu/homes/guo29/projects/shadow.html
|
R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011
|
|
Real-time Specular Highlight Removal
|
Code
|
Illumination, Reflectance, and Shadow
|
http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip
|
Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010
|
|
Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences
|
Code
|
Illumination, Reflectance, and Shadow
|
http://www.cs.cmu.edu/~jlalonde/software.html#skyModel
|
J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009
|
|
Sparse Coding for Image Classification
|
Code
|
Image Classification
|
http://www.ifp.illinois.edu/~jyang29/ScSPM.htm
|
J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009
|
|
Texture Classification
|
Code
|
Image Classification
|
http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html
|
M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005
|
|
Locality-constrained Linear Coding
|
Code
|
Image Classification
|
http://www.ifp.illinois.edu/~jyang29/LLC.htm
|
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010
|
|
Spatial Pyramid Matching
|
Code
|
Image Classification
|
http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip
|
S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006
|
|
Non-blind deblurring (and blind denoising) with integrated noise estimation
|
Code
|
Image Deblurring
|
http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm
|
U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011
|
|
Richardson-Lucy Deblurring for Scenes under Projective Motion Path
|
Code
|
Image Deblurring
|
http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip
|
Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011
|
|
Analyzing spatially varying blur
|
Code
|
Image Deblurring
|
http://www.eecs.harvard.edu/~ayanc/svblur/
|
A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010
|
|
Radon Transform
|
Code
|
Image Deblurring
|
http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip
|
T. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011
|
|
Eficient Marginal Likelihood Optimization in Blind Deconvolution
|
Code
|
Image Deblurring
|
http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip
|
A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011
|
|
BLS-GSM
|
Code
|
Image Denoising
|
http://decsai.ugr.es/~javier/denoise/
|
|
Gaussian Field of Experts
|
Code
|
Image Denoising
|
http://www.cs.huji.ac.il/~yweiss/BRFOE.zip
|
|
Field of Experts
|
Code
|
Image Denoising
|
http://www.cs.brown.edu/~roth/research/software.html
|
|
BM3D
|
Code
|
Image Denoising
|
http://www.cs.tut.fi/~foi/GCF-BM3D/
|
|
Nonlocal means with cluster trees
|
Code
|
Image Denoising
|
http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip
|
T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008
|
|
Non-local Means
|
Code
|
Image Denoising
|
http://dmi.uib.es/~abuades/codis/NLmeansfilter.m
|
|
K-SVD
|
Code
|
Image Denoising
|
http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip
|
|
What makes a good model of natural images ?
|
Code
|
Image Denoising
|
http://www.cs.huji.ac.il/~yweiss/BRFOE.zip
|
Y. Weiss and W. T. Freeman, CVPR 2007
|
|
Clustering-based Denoising
|
Code
|
Image Denoising
|
http://users.soe.ucsc.edu/~priyam/K-LLD/
|
P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009
|
|
Sparsity-based Image Denoising
|
Code
|
Image Denoising
|
http://www.csee.wvu.edu/~xinl/CSR.html
|
W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011
|
|
Kernel Regressions
|
Code
|
Image Denoising
|
http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip
|
|
Learning Models of Natural Image Patches
|
Code
|
Image Denoising; Image Super-resolution; Image Deblurring
|
http://www.cs.huji.ac.il/~daniez/
|
D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011
|
|
Efficient Belief Propagation for Early Vision
|
Code
|
Image Denoising; Stereo Matching
|
http://www.cs.brown.edu/~pff/bp/
|
P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006
|
|
SVM for Edge-Preserving Filtering
|
Code
|
Image Filtering
|
http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip
|
Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering,
|
|
Local Laplacian Filters
|
Code
|
Image Filtering
|
http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip
|
S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011
|
|
Real-time O(1) Bilateral Filtering
|
Code
|
Image Filtering
|
http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip
|
Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering,
|
|
Image smoothing via L0 Gradient Minimization
|
Code
|
Image Filtering
|
http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip
|
L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011
|
|
Anisotropic Diffusion
|
Code
|
Image Filtering
|
http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik
|
P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990
|
|
Guided Image Filtering
|
Code
|
Image Filtering
|
http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar
|
K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010
|
|
Fast Bilateral Filter
|
Code
|
Image Filtering
|
http://people.csail.mit.edu/sparis/bf/
|
S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006
|
|
GradientShop
|
Code
|
Image Filtering
|
http://grail.cs.washington.edu/projects/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
|
|
Domain Transformation
|
Code
|
Image Filtering
|
http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip
|
E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011
|
|
Weighted Least Squares Filter
|
Code
|
Image Filtering
|
http://www.cs.huji.ac.il/~danix/epd/
|
Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008
|
|
Piotr's Image & Video Matlab Toolbox
|
Code
|
Image Processing; Image Filtering
|
http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
|
Piotr Dollar, Piotr's Image & Video Matlab Toolbox,http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
|
|
Structural SIMilarity
|
Code
|
Image Quality Assessment
|
https://ece.uwaterloo.ca/~z70wang/research/ssim/
|
|
SPIQA
|
Code
|
Image Quality Assessment
|
http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip
|
|
Feature SIMilarity Index
|
Code
|
Image Quality Assessment
|
http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm
|
|
Degradation Model
|
Code
|
Image Quality Assessment
|
http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html
|
|
Tools and Methods for Image Registration
|
Tutorial
|
Image Registration
|
http://www.imgfsr.com/CVPR2011/Tutorial6/
|
Brown, G. Carneiro, A. A. Farag, E. Hancock, A. A. Goshtasby (Organizer), J. Matas, J.M. Morel, N. S. Netanyahu, F. Sur, and G. Yu, CVPR 2011 Tutorial
|
|
SLIC Superpixels
|
Code
|
Image Segmentation
|
http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html
|
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010
|
|
Recovering Occlusion Boundaries from a Single Image
|
Code
|
Image Segmentation
|
http://www.cs.cmu.edu/~dhoiem/software/
|
D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007.
|
|
Multiscale Segmentation Tree
|
Code
|
Image Segmentation
|
http://vision.ai.uiuc.edu/segmentation
|
E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009; N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996
|
|
Quick-Shift
|
Code
|
Image Segmentation
|
http://www.vlfeat.org/overview/quickshift.html
|
A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008
|
|
Efficient Graph-based Image Segmentation - C++ code
|
Code
|
Image Segmentation
|
http://people.cs.uchicago.edu/~pff/segment/
|
P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004
|
|
Turbepixels
|
Code
|
Image Segmentation
|
http://www.cs.toronto.edu/~babalex/research.html
|
A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009
|
|
Superpixel by Gerg Mori
|
Code
|
Image Segmentation
|
http://www.cs.sfu.ca/~mori/research/superpixels/
|
X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003
|
|
Normalized Cut
|
Code
|
Image Segmentation
|
http://www.cis.upenn.edu/~jshi/software/
|
J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000
|
|
Mean-Shift Image Segmentation - Matlab Wrapper
|
Code
|
Image Segmentation
|
http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz
|
D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002
|
|
Segmenting Scenes by Matching Image Composites
|
Code
|
Image Segmentation
|
http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html
|
B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009
|
|
OWT-UCM Hierarchical Segmentation
|
Code
|
Image Segmentation
|
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
|
P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011
|
|
Entropy Rate Superpixel Segmentation
|
Code
|
Image Segmentation
|
http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip
|
M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011
|
|
Efficient Graph-based Image Segmentation - Matlab Wrapper
|
Code
|
Image Segmentation
|
http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation
|
P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004
|
|
Biased Normalized Cut
|
Code
|
Image Segmentation
|
http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/
|
S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011
|
|
Segmentation by Minimum Code Length
|
Code
|
Image Segmentation
|
http://perception.csl.uiuc.edu/coding/image_segmentation/
|
A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007
|
|
Mean-Shift Image Segmentation - EDISON
|
Code
|
Image Segmentation
|
http://coewww.rutgers.edu/riul/research/code/EDISON/index.html
|
D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002
|
|
Self-Similarities for Single Frame Super-Resolution
|
Code
|
Image Super-resolution
|
https://eng.ucmerced.edu/people/cyang35/ACCV10.zip
|
C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010
|
|
MRF for image super-resolution
|
Code
|
Image Super-resolution
|
http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html
|
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
|
Sprarse coding super-resolution
|
Code
|
Image Super-resolution
|
http://www.ifp.illinois.edu/~jyang29/ScSR.htm
|
J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010
|
|
Multi-frame image super-resolution
|
Code
|
Image Super-resolution
|
http://www.robots.ox.ac.uk/~vgg/software/SR/index.html
|
Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis
|
|
Single-Image Super-Resolution Matlab Package
|
Code
|
Image Super-resolution
|
http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip
|
R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010
|
|
MDSP Resolution Enhancement Software
|
Code
|
Image Super-resolution
|
http://users.soe.ucsc.edu/~milanfar/software/superresolution.html
|
S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004
|
|
Nonparametric Scene Parsing via Label Transfer
|
Code
|
Image Understanding
|
http://people.csail.mit.edu/celiu/LabelTransfer/index.html
|
C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011
|
|
Discriminative Models for Multi-Class Object Layout
|
Code
|
Image Understanding
|
http://www.ics.uci.edu/~desaic/multiobject_context.zip
|
C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011
|
|
Towards Total Scene Understanding
|
Code
|
Image Understanding
|
http://vision.stanford.edu/projects/totalscene/index.html
|
L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009
|
|
Object Bank
|
Code
|
Image Understanding
|
http://vision.stanford.edu/projects/objectbank/index.html
|
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
|
|
SuperParsing
|
Code
|
Image Understanding
|
http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip
|
J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image
|
|
Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics
|
Code
|
Image Understanding
|
http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads
|
A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010
|
|
Information Theory
|
Talk
|
Information Theory
|
http://videolectures.net/mlss09uk_mackay_it/
|
David MacKay, University of Cambridge
|
|
Information Theory in Learning and Control
|
Talk
|
Information Theory
|
http://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfu
|
Naftali (Tali) Tishby, The Hebrew University
|
|
Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1)
|
Code
|
Kernels and Distances
|
http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip
|
H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007
|
|
Machine learning and kernel methods for computer vision
|
Talk
|
Kernels and Distances
|
http://videolectures.net/etvc08_bach_mlakm/
|
Francis R. Bach, INRIA
|
|
Diffusion-based distance
|
Code
|
Kernels and Distances
|
http://www.dabi.temple.edu/~hbling/code/DD_v1.zip
|
H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006
|
|
Fast Directional Chamfer Matching
|
Code
|
Kernels and Distances
|
http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip
|
|
Learning and Inference in Low-Level Vision
|
Talk
|
Low-level vision
|
http://videolectures.net/nips09_weiss_lil/
|
Yair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalem
|
|
TILT: Transform Invariant Low-rank Textures
|
Code
|
Low-Rank Modeling
|
http://perception.csl.uiuc.edu/matrix-rank/tilt.html
|
Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011
|
|
Low-Rank Matrix Recovery and Completion
|
Code
|
Low-Rank Modeling
|
http://perception.csl.uiuc.edu/matrix-rank/sample_code.html
|
|
RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition
|
Code
|
Low-Rank Modeling
|
http://perception.csl.uiuc.edu/matrix-rank/rasl.html
|
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
|
|
Statistical Pattern Recognition Toolbox
|
Code
|
Machine Learning
|
http://cmp.felk.cvut.cz/cmp/software/stprtool/
|
M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002
|
|
FastICA package for MATLAB
|
Code
|
Machine Learning
|
http://research.ics.tkk.fi/ica/fastica/
|
http://research.ics.tkk.fi/ica/book/
|
|
Boosting Resources by Liangliang Cao
|
Code
|
Machine Learning
|
http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm
|
http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm
|
|
Netlab Neural Network Software
|
Code
|
Machine Learning
|
http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/
|
C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995
|
|
Matlab Tutorial
|
Tutorial
|
Matlab
|
http://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.html
|
David Kriegman and Serge Belongie
|
|
Writing Fast MATLAB Code
|
Tutorial
|
Matlab
|
http://www.mathworks.com/matlabcentral/fileexchange/5685
|
Pascal Getreuer, Yale University
|
|
MRF Minimization Evaluation
|
Code
|
MRF Optimization
|
http://vision.middlebury.edu/MRF/
|
R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008
|
|
Max-flow/min-cut
|
Code
|
MRF Optimization
|
http://vision.csd.uwo.ca/code/maxflow-v3.01.zip
|
Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004
|
|
Planar Graph Cut
|
Code
|
MRF Optimization
|
http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip
|
F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009
|
|
Max-flow/min-cut for massive grids
|
Code
|
MRF Optimization
|
http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip
|
A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008
|
|
Multi-label optimization
|
Code
|
MRF Optimization
|
http://vision.csd.uwo.ca/code/gco-v3.0.zip
|
Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001
|
|
Max-flow/min-cut for shape fitting
|
Code
|
MRF Optimization
|
http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip
|
V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007
|
|
MILIS
|
Code
|
Multiple Instance Learning
|
|
Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010
|
|
MILES
|
Code
|
Multiple Instance Learning
|
http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/
|
Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006
|
|
MIForests
|
Code
|
Multiple Instance Learning
|
http://www.ymer.org/amir/software/milforests/
|
C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010
|
|
DD-SVM
|
Code
|
Multiple Instance Learning
|
|
Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004
|
|
DOGMA
|
Code
|
Multiple Kernel Learning
|
http://dogma.sourceforge.net/
|
F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010
|
|
SHOGUN
|
Code
|
Multiple Kernel Learning
|
http://www.shogun-toolbox.org/
|
S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006
|
|
SimpleMKL
|
Code
|
Multiple Kernel Learning
|
http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html
|
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008
|
|
OpenKernel.org
|
Code
|
Multiple Kernel Learning
|
http://www.openkernel.org/
|
F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011
|
|
Matlab Functions for Multiple View Geometry
|
Code
|
Multiple View Geometry
|
http://www.robots.ox.ac.uk/~vgg/hzbook/code/
|
|
for Computer Vision and Image Processing
|
Code
|
Multiple View Geometry
|
http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html
|
P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns
|
|
Patch-based Multi-view Stereo Software
|
Code
|
Multi-View Stereo
|
http://grail.cs.washington.edu/software/pmvs/
|
Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009
|
|
Clustering Views for Multi-view Stereo
|
Code
|
Multi-View Stereo
|
http://grail.cs.washington.edu/software/cmvs/
|
Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010
|
|
Multi-View Stereo Evaluation
|
Code
|
Multi-View Stereo
|
http://vision.middlebury.edu/mview/
|
S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006
|
|
Spectral Hashing
|
Code
|
Nearest Neighbors Matching
|
http://www.cs.huji.ac.il/~yweiss/SpectralHashing/
|
Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008
|
|
FLANN: Fast Library for Approximate Nearest Neighbors
|
Code
|
Nearest Neighbors Matching
|
http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
|
|
ANN: Approximate Nearest Neighbor Searching
|
Code
|
Nearest Neighbors Matching
|
http://www.cs.umd.edu/~mount/ANN/
|
|
LDAHash: Binary Descriptors for Matching in Large Image Databases
|
Code
|
Nearest Neighbors Matching
|
http://cvlab.epfl.ch/research/detect/ldahash/index.php
|
C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011.
|
|
Coherency Sensitive Hashing
|
Code
|
Nearest Neighbors Matching
|
http://www.eng.tau.ac.il/~simonk/CSH/index.html
|
S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011
|
|
Learning in Hierarchical Architectures: from Neuroscience to Derived Kernels
|
Talk
|
Neuroscience
|
http://videolectures.net/mlss09us_poggio_lhandk/
|
Tomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technology
|
|
Computer vision fundamentals: robust non-linear least-squares and their applications
|
Tutorial
|
Non-linear Least Squares
|
http://cvlab.epfl.ch/~fua/courses/lsq/
|
Pascal Fua, Vincent Lepetit, ICCV 2011 Tutorial
|
|
Non-rigid registration and reconstruction
|
Tutorial
|
Non-rigid registration
|
http://www.isr.ist.utl.pt/~adb/tutorial/
|
Alessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorial
|
|
Geometry constrained parts based detection
|
Tutorial
|
Object Detection
|
http://ci2cv.net/tutorials/iccv-2011/
|
Simon Lucey, Jason Saragih, ICCV 2011 Tutorial
|
|
Max-Margin Hough Transform
|
Code
|
Object Detection
|
http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/
|
S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009
|
|
Recognition using regions
|
Code
|
Object Detection
|
http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip
|
C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009
|
|
Poselet
|
Code
|
Object Detection
|
http://www.eecs.berkeley.edu/~lbourdev/poselets/
|
L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009
|
|
A simple object detector with boosting
|
Code
|
Object Detection
|
http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
|
ICCV 2005 short courses on Recognizing and Learning Object Categories
|
|
Feature Combination
|
Code
|
Object Detection
|
http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html
|
P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009
|
|
Hough Forests for Object Detection
|
Code
|
Object Detection
|
http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html
|
J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009
|
|
Cascade Object Detection with Deformable Part Models
|
Code
|
Object Detection
|
http://people.cs.uchicago.edu/~rbg/star-cascade/
|
P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010
|
|
Discriminatively Trained Deformable Part Models
|
Code
|
Object Detection
|
http://people.cs.uchicago.edu/~pff/latent/
|
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
|
|
A simple parts and structure object detector
|
Code
|
Object Detection
|
http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html
|
ICCV 2005 short courses on Recognizing and Learning Object Categories
|
|
Object Recognition with Deformable Models
|
Talk
|
Object Detection
|
http://www.youtube.com/watch?v=_J_clwqQ4gI
|
Pedro Felzenszwalb, Brown University
|
|
Ensemble of Exemplar-SVMs for Object Detection and Beyond
|
Code
|
Object Detection
|
http://www.cs.cmu.edu/~tmalisie/projects/iccv11/
|
T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011
|
|
Viola-Jones Object Detection
|
Code
|
Object Detection
|
http://pr.willowgarage.com/wiki/FaceDetection
|
P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001
|
|
Implicit Shape Model
|
Code
|
Object Detection
|
http://www.vision.ee.ethz.ch/~bleibe/code/ism.html
|
B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008
|
|
Multiple Kernels
|
Code
|
Object Detection
|
http://www.robots.ox.ac.uk/~vgg/software/MKL/
|
A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009
|
|
Ensemble of Exemplar-SVMs
|
Code
|
Object Detection
|
http://www.cs.cmu.edu/~tmalisie/projects/iccv11/
|
T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011
|
|
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
|
Code
|
Object Discovery
|
http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html
|
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
|
|
Objectness measure
|
Code
|
Object Proposal
|
http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz
|
B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010
|
|
Parametric min-cut
|
Code
|
Object Proposal
|
http://sminchisescu.ins.uni-bonn.de/code/cpmc/
|
J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010
|
|
Region-based Object Proposal
|
Code
|
Object Proposal
|
http://vision.cs.uiuc.edu/proposals/
|
I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010
|
|
Biologically motivated object recognition
|
Code
|
Object Recognition
|
http://cbcl.mit.edu/software-datasets/standardmodel/index.html
|
T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005
|
|
Recognition by Association via Learning Per-exemplar Distances
|
Code
|
Object Recognition
|
http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz
|
T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008
|
|
Sparse to Dense Labeling
|
Code
|
Object Segmentation
|
http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz
|
P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011
|
|
ClassCut for Unsupervised Class Segmentation
|
Code
|
Object Segmentation
|
http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip
|
B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010
|
|
Geodesic Star Convexity for Interactive Image Segmentation
|
Code
|
Object Segmentation
|
http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml
|
V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation
|
|
Black and Anandan's Optical Flow
|
Code
|
Optical Flow
|
http://www.cs.brown.edu/~dqsun/code/ba.zip
|
|
Optical Flow Evaluation
|
Code
|
Optical Flow
|
http://vision.middlebury.edu/flow/
|
S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011
|
|
Optical Flow by Deqing Sun
|
Code
|
Optical Flow
|
http://www.cs.brown.edu/~dqsun/code/flow_code.zip
|
D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010
|
|
Horn and Schunck's Optical Flow
|
Code
|
Optical Flow
|
http://www.cs.brown.edu/~dqsun/code/hs.zip
|
|
Dense Point Tracking
|
Code
|
Optical Flow
|
http://lmb.informatik.uni-freiburg.de/resources/binaries/
|
N. Sundaram, T. Brox, K. Keutzer
|
|
Large Displacement Optical Flow
|
Code
|
Optical Flow
|
http://lmb.informatik.uni-freiburg.de/resources/binaries/
|
T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011
|
|
Classical Variational Optical Flow
|
Code
|
Optical Flow
|
http://lmb.informatik.uni-freiburg.de/resources/binaries/
|
T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004
|
|
Optimization Algorithms in Machine Learning
|
Talk
|
Optimization
|
http://videolectures.net/nips2010_wright_oaml/
|
Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison
|
|
Convex Optimization
|
Talk
|
Optimization
|
http://videolectures.net/mlss2011_vandenberghe_convex/
|
Lieven Vandenberghe, Electrical Engineering Department, University of California, Los Angeles
|
|
Energy Minimization with Label costs and Applications in Multi-Model Fitting
|
Talk
|
Optimization
|
http://videolectures.net/nipsworkshops2010_boykov_eml/
|
Yuri Boykov, Department of Computer Science, University of Western Ontario
|
|
Who is Afraid of Non-Convex Loss Functions?
|
Talk
|
Optimization
|
http://videolectures.net/eml07_lecun_wia/
|
Yann LeCun, New York University
|
|
Optimization Algorithms in Support Vector Machines
|
Talk
|
Optimization and Support Vector Machines
|
http://videolectures.net/mlss09us_wright_oasvm/
|
Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison
|
|
Training Deformable Models for Localization
|
Code
|
Pose Estimation
|
http://www.ics.uci.edu/~dramanan/papers/parse/index.html
|
Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006
|
|
Articulated Pose Estimation using Flexible Mixtures of Parts
|
Code
|
Pose Estimation
|
http://phoenix.ics.uci.edu/software/pose/
|
Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011
|
|
Calvin Upper-Body Detector
|
Code
|
Pose Estimation
|
http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/
|
E. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009
|
|
Estimating Human Pose from Occluded Images
|
Code
|
Pose Estimation
|
http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip
|
J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009
|
|
Relative Entropy
|
Talk
|
Relative Entropy
|
http://videolectures.net/nips09_verdu_re/
|
Sergio Verdu, Princeton University
|
|
Saliency-based video segmentation
|
Code
|
Saliency Detection
|
http://www.brl.ntt.co.jp/people/akisato/saliency3.html
|
K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009
|
|
Saliency Using Natural statistics
|
Code
|
Saliency Detection
|
http://cseweb.ucsd.edu/~l6zhang/
|
L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008
|
|
Context-aware saliency detection
|
Code
|
Saliency Detection
|
http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html
|
S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010.
|
|
Learning to Predict Where Humans Look
|
Code
|
Saliency Detection
|
http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html
|
T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009
|
|
Graph-based visual saliency
|
Code
|
Saliency Detection
|
http://www.klab.caltech.edu/~harel/share/gbvs.php
|
J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007
|
|
Discriminant Saliency for Visual Recognition from Cluttered Scenes
|
Code
|
Saliency Detection
|
http://www.svcl.ucsd.edu/projects/saliency/
|
D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004
|
|
Global Contrast based Salient Region Detection
|
Code
|
Saliency Detection
|
http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/
|
M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011
|
|
Itti, Koch, and Niebur' saliency detection
|
Code
|
Saliency Detection
|
http://www.saliencytoolbox.net/
|
L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998
|
|
Learning Hierarchical Image Representation with Sparsity, Saliency and Locality
|
Code
|
Saliency Detection
|
|
J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011
|
|
Spectrum Scale Space based Visual Saliency
|
Code
|
Saliency Detection
|
http://www.cim.mcgill.ca/~lijian/saliency.htm
|
J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011
|
|
Attention via Information Maximization
|
Code
|
Saliency Detection
|
http://www.cse.yorku.ca/~neil/AIM.zip
|
N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005
|
|
Saliency detection: A spectral residual approach
|
Code
|
Saliency Detection
|
http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html
|
X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007
|
|
Saliency detection using maximum symmetric surround
|
Code
|
Saliency Detection
|
http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html
|
R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010
|
|
Frequency-tuned salient region detection
|
Code
|
Saliency Detection
|
http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html
|
R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009
|
|
Segmenting salient objects from images and videos
|
Code
|
Saliency Detection
|
http://www.cse.oulu.fi/MVG/Downloads/saliency
|
E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010
|
|
Diffusion Geometry Methods in Shape Analysis
|
Tutorial
|
Shape Analysis, Diffusion Geometry
|
http://tosca.cs.technion.ac.il/book/course_eccv10.html
|
A. Brontein and M. Bronstein, ECCV 2010 Tutorial
|
|
Source Code Collection for Reproducible Research
|
Link
|
Source code
|
http://www.csee.wvu.edu/~xinl/reproducible_research.html
|
collected by Xin Li, Lane Dept of CSEE, West Virginia University
|
|
Computer Vision Algorithm Implementations
|
Link
|
Source code
|
http://www.cvpapers.com/rr.html
|
CVPapers
|
|
Robust Sparse Coding for Face Recognition
|
Code
|
Sparse Representation
|
http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip
|
M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011
|
|
Sparse coding simulation software
|
Code
|
Sparse Representation
|
http://redwood.berkeley.edu/bruno/sparsenet/
|
Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996
|
|
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
|
Code
|
Sparse Representation
|
http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar
|
M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
|
|
Fisher Discrimination Dictionary Learning for Sparse Representation
|
Code
|
Sparse Representation
|
http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip
|
M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011
|
|
Efficient sparse coding algorithms
|
Code
|
Sparse Representation
|
http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm
|
H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007
|
|
A Linear Subspace Learning Approach via Sparse Coding
|
Code
|
Sparse Representation
|
http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip
|
L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011
|
|
SPArse Modeling Software
|
Code
|
Sparse Representation
|
http://www.di.ens.fr/willow/SPAMS/
|
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010
|
|
Sparse Methods for Machine Learning: Theory and Algorithms
|
Talk
|
Sparse Representation
|
http://videolectures.net/nips09_bach_smm/
|
Francis R. Bach, INRIA
|
|
Centralized Sparse Representation for Image Restoration
|
Code
|
Sparse Representation
|
http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip
|
W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011
|
|
A Tutorial on Spectral Clustering
|
Tutorial
|
Spectral Clustering
|
http://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdf
|
Ulrike von Luxburg, Max Planck Institute for Biological Cybernetics
|
|
Statistical Learning Theory
|
Talk
|
Statistical Learning Theory
|
http://videolectures.net/mlss04_taylor_slt/
|
John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
|
|
Stereo Evaluation
|
Code
|
Stereo
|
http://vision.middlebury.edu/stereo/
|
D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001
|
|
Constant-Space Belief Propagation
|
Code
|
Stereo
|
http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm
|
Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010
|
|
libmv
|
Code
|
Structure from motion
|
http://code.google.com/p/libmv/
|
|
Structure from Motion toolbox for Matlab by Vincent Rabaud
|
Code
|
Structure from motion
|
http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/
|
|
FIT3D
|
Code
|
Structure from motion
|
http://www.fit3d.info/
|
|
VisualSFM : A Visual Structure from Motion System
|
Code
|
Structure from motion
|
http://www.cs.washington.edu/homes/ccwu/vsfm/
|
|
Structure and Motion Toolkit in Matlab
|
Code
|
Structure from motion
|
http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm
|
|
Nonrigid Structure from Motion
|
Tutorial
|
Structure from motion
|
http://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.html
|
Y. Sheikh and Sohaib Khan, ECCV 2010 Tutorial
|
|
Bundler
|
Code
|
Structure from motion
|
http://phototour.cs.washington.edu/bundler/
|
N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006
|
|
Nonrigid Structure From Motion in Trajectory Space
|
Code
|
Structure from motion
|
http://cvlab.lums.edu.pk/nrsfm/index.html
|
|
OpenSourcePhotogrammetry
|
Code
|
Structure from motion
|
http://opensourcephotogrammetry.blogspot.com/
|
|
Structured Prediction and Learning in Computer Vision
|
Tutorial
|
Structured Prediction
|
http://www.nowozin.net/sebastian/cvpr2011tutorial/
|
S. Nowozin and C. Lampert, CVPR 2011 Tutorial
|
|
Generalized Principal Component Analysis
|
Code
|
Subspace Learning
|
http://www.vision.jhu.edu/downloads/main.php?dlID=c1
|
R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003
|
|
Text recognition in the wild
|
Code
|
Text Recognition
|
http://vision.ucsd.edu/~kai/grocr/
|
K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011
|
|
Neocognitron for handwritten digit recognition
|
Code
|
Text Recognition
|
http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375
|
K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003
|
|
Image Quilting for Texture Synthesis and Transfer
|
Code
|
Texture Synthesis
|
http://www.cs.cmu.edu/~efros/quilt_research_code.zip
|
A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001
|
|
Variational methods for computer vision
|
Tutorial
|
Variational Calculus
|
http://cvpr.in.tum.de/tutorials/iccv2011
|
Daniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorial
|
|
Variational Methods in Computer Vision
|
Tutorial
|
Variational Calculus
|
http://cvpr.cs.tum.edu/tutorials/eccv2010
|
D. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorial
|
|
Understanding Visual Scenes
|
Talk
|
Visual Recognition
|
http://videolectures.net/nips09_torralba_uvs/
|
Antonio Torralba, MIT
|
|
Visual Recognition, University of Texas at Austin, Fall 2011
|
Course
|
Visual Recognition
|
http://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.html
|
Kristen Grauman
|
|
Tracking using Pixel-Wise Posteriors
|
Code
|
Visual Tracking
|
http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml
|
C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008
|
|
Visual Tracking with Histograms and Articulating Blocks
|
Code
|
Visual Tracking
|
http://www.cise.ufl.edu/~smshahed/tracking.htm
|
S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008
|
|
Lucas-Kanade affine template tracking
|
Code
|
Visual Tracking
|
http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking
|
S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002
|
|
Visual Tracking Decomposition
|
Code
|
Visual Tracking
|
http://cv.snu.ac.kr/research/~vtd/
|
J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010
|
|
GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker
|
Code
|
Visual Tracking
|
http://cs.unc.edu/~ssinha/Research/GPU_KLT/
|
S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007
|
|
Motion Tracking in Image Sequences
|
Code
|
Visual Tracking
|
http://www.cs.berkeley.edu/~flw/tracker/
|
C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000
|
|
Particle Filter Object Tracking
|
Code
|
Visual Tracking
|
http://blogs.oregonstate.edu/hess/code/particles/
|
|
Tracking with Online Multiple Instance Learning
|
Code
|
Visual Tracking
|
http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
|
B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011
|
|
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker
|
Code
|
Visual Tracking
|
http://www.ces.clemson.edu/~stb/klt/
|
B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981
|
|
Superpixel Tracking
|
Code
|
Visual Tracking
|
http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html
|
S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011
|
|
L1 Tracking
|
Code
|
Visual Tracking
|
http://www.dabi.temple.edu/~hbling/code_data.htm
|
X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009
|
|
Online Discriminative Object Tracking with Local Sparse Representation
|
Code
|
Visual Tracking
|
http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip
|
Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012
|
|
Incremental Learning for Robust Visual Tracking
|
Code
|
Visual Tracking
|
http://www.cs.toronto.edu/~dross/ivt/
|
D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007
|
|
Online boosting trackers
|
Code
|
Visual Tracking
|
http://www.vision.ee.ethz.ch/boostingTrackers/
|
H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006
|
|
Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects
|
Code
|
Visual Tracking
|
http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz
|
H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011
|
|
Object Tracking
|
Code
|
Visual Tracking
|
http://plaza.ufl.edu/lvtaoran/object%20tracking.htm
|
A. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006
|
|