CV codes代码分类整理合集(http://www.sigvc.org/bbs/thread-72-1-1.html)

一、特征提取Feature Extraction：
   SIFT [1] [Demo program][SIFT Library] [VLFeat]
   PCA-SIFT [2] [Project]
   Affine-SIFT [3] [Project]
   SURF [4] [OpenSURF] [Matlab Wrapper]
   Affine Covariant Features [5] [Oxford project]
   MSER [6] [Oxford project] [VLFeat]
   Geometric Blur [7] [Code]
   Local Self-Similarity Descriptor [8] [Oxford implementation]
   Global and Efficient Self-Similarity [9] [Code]
   Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]
   GIST [11] [Project]
   Shape Context [12] [Project]
   Color Descriptor [13] [Project]
   Pyramids of Histograms of Oriented Gradients [Code]
   Space-Time Interest Points (STIP) [14][Project] [Code]
   Boundary Preserving Dense Local Regions [15][Project]
   Weighted Histogram[Code]
   Histogram-based Interest Points Detectors[Paper][Code]
   An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]
   Fast Sparse Representation with Prototypes[Project]
   Corner Detection [Project]
   AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
二、图像分割Image Segmentation：
     Normalized Cut [1] [Matlab code]
     Gerg Mori’ Superpixel code [2] [Matlab code]
     Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
     Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
     OWT-UCM Hierarchical Segmentation [5] [Resources]
     Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
     Quick-Shift [7] [VLFeat]
     SLIC Superpixels [8] [Project]
     Segmentation by Minimum Code Length [9] [Project]
     Biased Normalized Cut [10] [Project]
     Segmentation Tree [11-12] [Project]
     Entropy Rate Superpixel Segmentation [13] [Code]
     Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
     Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
     Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
     Random Walks for Image Segmentation[Paper][Code]
     Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
     An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
     Geodesic Star Convexity for Interactive Image Segmentation[Project]
     Contour Detection and Image Segmentation Resources[Project][Code]
     Biased Normalized Cuts[Project]
     Max-flow/min-cut[Project]
     Chan-Vese Segmentation using Level Set[Project]
     A Toolbox of Level Set Methods[Project]
     Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
     Improved C-V active contour model[Paper][Code]
     A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
    Level Set Method Research by Chunming Li[Project]
三、目标检测Object Detection：
     A simple object detector with boosting [Project]
     INRIA Object Detection and Localization Toolkit [1] [Project]
     Discriminatively Trained Deformable Part Models [2] [Project]
     Cascade Object Detection with Deformable Part Models [3] [Project]
     Poselet [4] [Project]
     Implicit Shape Model [5] [Project]
     Viola and Jones’s Face Detection [6] [Project]
     Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]
     Hand detection using multiple proposals[Project]
     Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]
     Discriminatively trained deformable part models[Project]
     Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]
     Image Processing On Line[Project]
     Robust Optical Flow Estimation[Project]
     Where's Waldo: Matching People in Images of Crowds[Project]
四、显著性检测Saliency Detection：
     Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]
     Frequency-tuned salient region detection [2] [Project]
     Saliency detection using maximum symmetric surround [3] [Project]
     Attention via Information Maximization [4] [Matlab code]
     Context-aware saliency detection [5] [Matlab code]
     Graph-based visual saliency [6] [Matlab code]
     Saliency detection: A spectral residual approach. [7] [Matlab code]
     Segmenting salient objects from images and videos. [8] [Matlab code]
     Saliency Using Natural statistics. [9] [Matlab code]
     Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]
     Learning to Predict Where Humans Look [11] [Project]
     Global Contrast based Salient Region Detection [12] [Project]
     Bayesian Saliency via Low and Mid Level Cues[Project]
     Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]
五、图像分类、聚类Image Classification, Clustering
     Pyramid Match [1] [Project]
     Spatial Pyramid Matching [2] [Code]
     Locality-constrained Linear Coding [3] [Project] [Matlab code]
     Sparse Coding [4] [Project] [Matlab code]
     Texture Classification [5] [Project]
     Multiple Kernels for Image Classification [6] [Project]
     Feature Combination [7] [Project]
     SuperParsing [Code]
     Large Scale Correlation Clustering Optimization[Matlab code]
     Detecting and Sketching the Common[Project]
     Self-Tuning Spectral Clustering[Project][Code]
     User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]
     Filters for Texture Classification[Project]
     Multiple Kernel Learning for Image Classification[Project]
    SLIC Superpixels[Project]
六、抠图Image Matting
     A Closed Form Solution to Natural Image Matting [Code]
     Spectral Matting [Project]
     Learning-based Matting [Code]
七、目标跟踪Object Tracking：
     A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]
     Object Tracking via Partial Least Squares Analysis[Paper][Code]
     Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]
     Online Visual Tracking with Histograms and Articulating Blocks[Project]
     Incremental Learning for Robust Visual Tracking[Project]
     Real-time Compressive Tracking[Project]
     Robust Object Tracking via Sparsity-based Collaborative Model[Project]
     Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]
     Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]
     Superpixel Tracking[Project]
     Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]
     Online Multiple Support Instance Tracking [Paper][Code]
     Visual Tracking with Online Multiple Instance Learning[Project]
     Object detection and recognition[Project]
     Compressive Sensing Resources[Project]
     Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]
     Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
     the HandVu：vision-based hand gesture interface[Project]
八、Kinect：
     Kinect toolbox[Project]
     OpenNI[Project]
     zouxy09 CSDN Blog[Resource]
九、3D相关：
     3D Reconstruction of a Moving Object[Paper] [Code]
     Shape From Shading Using Linear Approximation[Code]
     Combining Shape from Shading and Stereo Depth Maps[Project][Code]
     Shape from Shading: A Survey[Paper][Code]
     A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
     Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
     A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
     Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
     Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
     Learning 3-D Scene Structure from a Single Still Image[Project]
十、机器学习算法：
     Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]
     Random Sampling[code]
     Probabilistic Latent Semantic Analysis (pLSA)[Code]
     FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
     Fast Intersection / Additive Kernel SVMs[Project]
     SVM[Code]
     Ensemble learning[Project]
     Deep Learning[Net]
     Deep Learning Methods for Vision[Project]
     Neural Network for Recognition of Handwritten Digits[Project]
     Training a deep autoencoder or a classifier on MNIST digits[Project]
    THE MNIST DATABASE of handwritten digits[Project]
    Ersatz：deep neural networks in the cloud[Project]
    Deep Learning [Project]
    sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
    Weka 3: Data Mining Software in Java[Project]
    Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]
    CNN - Convolutional neural network class[Matlab Tool]
    Yann LeCun's Publications[Wedsite]
    LeNet-5, convolutional neural networks[Project]
    Training a deep autoencoder or a classifier on MNIST digits[Project]
    Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]
十一、目标、行为识别Object, Action Recognition：
     Action Recognition by Dense Trajectories[Project][Code]
     Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
     Recognition Using Regions[Paper][Code]
     2D Articulated Human Pose Estimation[Project]
     Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
     Estimating Human Pose from Occluded Images[Paper][Code]
     Quasi-dense wide baseline matching[Project]
     ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Prpject]
十二、图像处理：
     Distance Transforms of Sampled Functions[Project]
    The Computer Vision Homepage[Project]
十三、一些实用工具：
     EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
     a development kit of matlab mex functions for OpenCV library[Project]
     Fast Artificial Neural Network Library[Project]





https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

Maintained by Jia-Bin Huang

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

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