CV codes代码分类整理合集
2014-12-18 21:00
309 查看
转载来源：http://blog.csdn.net/tiandijun/article/details/24342425
原转自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
原转自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; | 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 |
内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理 
相关文章推荐
- CV codes代码分类整理合集 http://www.sigvc.org/bbs/thread-72-1-1.html
- CV codes代码分类整理合集
- CV codes代码分类整理合集(http://www.sigvc.org/bbs/thread-72-1-1.html)
- CV codes代码分类整理合集
- CV codes代码分类整理合集 《转》
- [转]CV codes代码分类整理合集
- CV codes代码分类整理合集
- (收藏）CV codes代码分类整理（CV codes代码分类整理合集 http://www.sigvc.org/bbs/thread-72-1-1.html）
- CV codes代码分类整理合集(http://www.sigvc.org/bbs/thread-72-1-1.html)
- CV codes代码分类整理合集
- 来自西弗吉利亚大学li xin整理的CV代码合集（转）
- CV codes分类整理集合
- 来自西弗吉利亚大学li xin整理的CV代码合集
- 来自西弗吉利亚大学li xin整理的CV代码合集
- 用Hive+Hadoop集群实现《飞机票购买人群分类案例》思路+代码 （实验数据待整理）
- 自己的技术经验和心得，及常用的资料和代码如何整理、分类和保存才好呢？
- 图像处理中项目代码合集，包括特征提取-图像分割-分类-匹配-降噪等等
- 来自西弗吉利亚大学li xin整理的CV代码合集
- Codeforces 题目合集+分类+代码 【Updating...】【351 in total】
- 来自西弗吉利亚大学li xin整理的CV代码合集