一、特徵提取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、影象分割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、目標檢測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、顯著性檢測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、影象分類、聚類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、摳圖Image Matting
     A Closed Form Solution to Natural Image Matting [Code]
     Spectral Matting [Project]
     Learning-based Matting [Code]
七、目標跟蹤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相關：
     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、機器學習演算法：
     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 (餘凱)[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、目標、行為識別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、影象處理：
     Distance Transforms of Sampled Functions[Project]
    The Computer Vision Homepage[Project]
十三、一些實用工具：
     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

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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