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]

• Real-time Facial Feature Detection using Conditional Regression Forests[Project]

• Global and Efficient Self-Similarity for Object Classification and Detection[code]

• WαSH: Weighted α-Shapes for Local Feature Detection[Project]

• HOG[Project]

• Online Selection of Discriminative Tracking Features[Project]

  
  

二、圖像分割I(lǐng)mage 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]

• Ef?cient 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]

• ClassCut for Unsupervised Class Segmentation[code]

• SEEDS: Superpixels Extracted via Energy-Driven Sampling?[Project][other]

  
  

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]

• Scalable Multi-class Object Detection[Project]

• Class-Specific Hough Forests for Object Detection[Project]

• Deformed Lattice Detection In Real-World Images[Project]

• Discriminatively trained deformable part models[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]

• Saliency Detection: A Spectral Residual Approach[Code]

  
  

五、圖像分類、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]

• Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]

  
  

八、Kinect：

• Kinect toolbox[Project]

• OpenNI[Project]

• zouxy09 CSDN Blog[Resource]

• FingerTracker 手指跟蹤[code]

  
  

九、3D相關(guān)：

• 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(xué)習算法：

• 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]

• Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]

• Sparse coding simulation software[Project]

• Visual Recognition and Machine Learning Summer School[Software]

  
  

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[Project]

• Real Time Head Pose Estimation with Random Regression Forests[Project]

• 2D Action Recognition Serves 3D Human Pose Estimation[

• A Hough Transform-Based Voting Framework for Action Recognition[

• Motion Interchange Patterns for Action Recognition in Unconstrained Videos[

• 2D articulated human pose estimation software[Project]

• Learning and detecting shape models [code]

• Progressive Search Space Reduction for Human Pose Estimation[Project]

• Learning Non-Rigid 3D Shape from 2D Motion[Project]

  
  

十二、圖像處理：

• Distance Transforms of Sampled Functions[Project]

• The Computer Vision Homepage[Project]

• Efficient appearance distances between windows[code]

• Image Exploration algorithm[code]

• Motion Magnification 運動放大 [Project]

• Bilateral Filtering for Gray and Color Images 雙邊濾波器 [Project]

• A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [

  
  

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]

  
  

十四、人手及指尖檢測與識別：

• finger-detection-and-gesture-recognition [Code]

• Hand and Finger Detection using JavaCV[Project]

• Hand and fingers detection[Code]

  
  

十五、場景解釋：

• Nonparametric Scene Parsing via Label Transfer [Project]

  
  

十六、光流Optical flow：

• High accuracy optical flow using a theory for warping [Project]

• Dense Trajectories Video Description [Project]

• SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]

• KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]

• Tracking Cars Using Optical Flow[Project]

• Secrets of optical flow estimation and their principles[Project]

• implmentation of the Black and Anandan dense optical flow method[Project]

• Optical Flow Computation[Project]

• Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]

• A Database and Evaluation Methodology for Optical Flow[Project]

• optical flow relative[Project]

• Robust Optical Flow Estimation [Project]

• optical flow[Project]

  
  

十七、圖像檢索Image Retrieval：

• Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval?[Paper][code]

  
  

十八、馬爾科夫隨機場Markov Random Fields：

• Markov Random Fields for Super-Resolution?[Project]

• A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]

  
  

十九、運動檢測Motion detection：

• Moving Object Extraction, Using Models or Analysis of Regions?[Project]

• Background Subtraction: Experiments and Improvements for ViBe [Project]

• A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]

• changedetection.net: A new change detection benchmark dataset[Project]

• ViBe - a powerful technique for background detection and subtraction in video sequences[Project]

• Background Subtraction Program[Project]

• Motion Detection Algorithms[Project]

• Stuttgart Artificial Background Subtraction Dataset[Project]

• Object Detection, Motion Estimation, and Tracking[Project]

  
  

  Feature Detection and Description

  General Libraries:

  • VLFeat?– Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors (including DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. See?Modern features: Software?– Slides providing a demonstration of VLFeat and also links to other software. Check also?VLFeat hands-on session training

  • OpenCV?– Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

    
    
  
  

  Fast Keypoint Detectors for Real-time Applications:

  • FAST?– High-speed corner detector implementation for a wide variety of platforms

  • AGAST?– Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV 2010).

    
    
  
  

  Binary Descriptors for Real-Time Applications:

  • BRIEF?– C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)

  • ORB?– OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)

  • BRISK?– Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)

  • FREAK?– Faster than BRISK (invariant to rotations and scale) (CVPR 2012)

    
    
  
  

  SIFT and SURF Implementations:

  • SIFT:?VLFeat,?OpenCV,?Original code?by David Lowe,?GPU implementation,?OpenSIFT

  • SURF:?Herbert Bay’s code,?OpenCV,?GPU-SURF

    
    
  
  

  Other Local Feature Detectors and Descriptors:

  • VGG Affine Covariant features?– Oxford code for various affine covariant feature detectors and descriptors.

  • LIOP descriptor?– Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).

  • Local Symmetry Features?– Source code for matching of local symmetry features under large variations in lighting, age, and rendering style (CVPR 2012).

    
    
  
  

  Global Image Descriptors:

  • GIST?– Matlab code for the GIST descriptor

  • CENTRIST?– Global visual descriptor for scene categorization and object detection (PAMI 2011)

    
    

  Feature Coding and Pooling

  • VGG Feature Encoding Toolkit?– Source code for various state-of-the-art feature encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding.

  • Spatial Pyramid Matching?– Source code for feature pooling based on spatial pyramid matching (widely used for image classification)

    
    

  Convolutional Nets and Deep Learning

  • EBLearn?– C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.

  • Torch7?– Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.

  • Deep Learning?- Various links for deep learning software.

    
    

  Part-Based Models

  • Deformable Part-based Detector?– Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)

  • Efficient Deformable Part-Based Detector?– Branch-and-Bound implementation for a deformable part-based detector.

  • Accelerated Deformable Part Model?– Efficient implementation of a method that achieves the exact same performance of deformable part-based detectors but with significant acceleration (ECCV 2012).

  • Coarse-to-Fine Deformable Part Model?– Fast approach for deformable object detection (CVPR 2011).

  • Poselets?– C++ and Matlab versions for object detection based on poselets.

  • Part-based Face Detector and Pose Estimation?– Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).

    
    

    Attributes and Semantic Features

    • Relative Attributes?– Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).

    • Object Bank?– Implementation of object bank semantic features (NIPS 2010). See alsoActionBank

    • Classemes, Picodes, and Meta-class features?– Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

      
      

    Large-Scale Learning

    • Additive Kernels?– Source code for fast additive kernel SVM classifiers (PAMI 2013).

    • LIBLINEAR?– Library for large-scale linear SVM classification.

    • VLFeat?– Implementation for Pegasos SVM and Homogeneous Kernel map.

      
      

    Fast Indexing and Image Retrieval

    • FLANN?– Library for performing fast approximate nearest neighbor.

    • Kernelized LSH?– Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).

    • ITQ Binary codes?– Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).

    • INRIA Image Retrieval?– Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

      
      

    Object Detection

    • See?Part-based Models?and?Convolutional Nets?above.

    • Pedestrian Detection at 100fps?– Very fast and accurate pedestrian detector (CVPR 2012).

    • Caltech Pedestrian Detection Benchmark?– Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.

    • OpenCV?– Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.

    • Efficient Subwindow Search?– Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

      
      

    3D Recognition

    • Point-Cloud Library?– Library for 3D image and point cloud processing.

      
      

    Action Recognition

    • ActionBank?– Source code for action recognition based on the ActionBank representation (CVPR 2012).

    • STIP Features?– software for computing space-time interest point descriptors

    • Independent Subspace Analysis?– Look for Stacked ISA for Videos (CVPR 2011)

    • Velocity Histories of Tracked Keypoints?- C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

      
      

    ---

    Datasets

    Attributes

    • Animals with Attributes?– 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.

    • aYahoo and aPascal?– Attribute annotations for images collected from Yahoo and Pascal VOC 2008.

    • FaceTracer?– 15,000 faces annotated with 10 attributes and fiducial points.

    • PubFig?– 58,797 face images of 200 people with 73 attribute classifier outputs.

    • [url=http://vis-www.cs.umass.edu/lfw/]LFW[/url]?– 13,233 face images of 5,749 people with 73 attribute classifier outputs.

    • Human Attributes?– 8,000 people with annotated attributes. Check also this?link?for another dataset of human attributes.

    • SUN Attribute Database?– Large-scale scene attribute database with a taxonomy of 102 attributes.

    • ImageNet Attributes?– Variety of attribute labels for the ImageNet dataset.

    • Relative attributes?– Data for OSR and a subset of PubFig datasets. Check also this?linkfor the WhittleSearch data.

    • Attribute Discovery Dataset?– Images of shopping categories associated with textual descriptions.

      
      

    Fine-grained Visual Categorization

    • Caltech-UCSD Birds Dataset?– Hundreds of bird categories with annotated parts and attributes.

    • Stanford Dogs Dataset?– 20,000 images of 120 breeds of dogs from around the world.

    • Oxford-IIIT Pet Dataset?– 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.

    • Leeds Butterfly Dataset?– 832 images of 10 species of butterflies.

    • Oxford Flower Dataset?– Hundreds of flower categories.

      
      

    Face Detection

    • [url=http://vis-www.cs.umass.edu/fddb/]FDDB[/url]?– UMass face detection dataset and benchmark (5,000+ faces)

    • CMU/MIT?– Classical face detection dataset.

      
      

    Face Recognition

    • Face Recognition Homepage?– Large collection of face recognition datasets.

    • [url=http://vis-www.cs.umass.edu/lfw/]LFW[/url]?– UMass unconstrained face recognition dataset (13,000+ face images).

    • NIST Face Homepage?– includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.

    • CMU Multi-PIE?– contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.

    • FERET?– Classical face recognition dataset.

    • Deng Cai’s face dataset in Matlab Format?– Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.

    • SCFace?– Low-resolution face dataset captured from surveillance cameras.

      
      

    Handwritten Digits

    • MNIST?– large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.

      
      

    Pedestrian Detection

    • Caltech Pedestrian Detection Benchmark?– 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.

    • INRIA Person Dataset?– Currently one of the most popular pedestrian detection datasets.

    • ETH Pedestrian Dataset?– Urban dataset captured from a stereo rig mounted on a stroller.

    • TUD-Brussels Pedestrian Dataset?– Dataset with image pairs recorded in an crowded urban setting with an onboard camera.

    • PASCAL Human Detection?– One of 20 categories in PASCAL VOC detection challenges.

    • USC Pedestrian Dataset?– Small dataset captured from surveillance cameras.

      
      

    Generic Object Recognition

    • ImageNet?– Currently the largest visual recognition dataset in terms of number of categories and images.

    • Tiny Images?– 80 million 32x32 low resolution images.

    • Pascal VOC?– One of the most influential visual recognition datasets.

    • Caltech 101?/?Caltech 256?– Popular image datasets containing 101 and 256 object categories, respectively.

    • MIT LabelMe?– Online annotation tool for building computer vision databases.

      
      

    Scene Recognition

    • MIT SUN Dataset?– MIT scene understanding dataset.

    • UIUC Fifteen Scene Categories?– Dataset of 15 natural scene categories.

      
      

    Feature Detection and Description

    • VGG Affine Dataset?– Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor an evaluation framework.

      
      

    Action Recognition

    • Benchmarking Activity Recognition?– CVPR 2012 tutorial covering various datasets for action recognition.

      
      

    RGBD Recognition

    • RGB-D Object Dataset?– Dataset containing 300 common household objects

      
      

    Reference:

    [1]:?http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html

    
    特征提取

    • SURF特征:?http://www.vision.ee.ethz.ch/software/index.de.html(當然這只是其中之一)

    • LBP特征(一種紋理特征)：http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html

    • Fast Corner Detection（OpenCV中的Fast算法）:FAST Corner Detection -- Edward Rosten

      
      
    機器視覺

    • A simple object detector with boosting(Awarded the Best Short Course Prize at ICCV 2005，So了解adaboost的推薦之作)：http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html

    • Boosting(該網(wǎng)頁上有相當全的Boosting的文章和幾個Boosting代碼，本人推薦)：http://cbio.mskcc.org/~aarvey/boosting_papers.html

    • Adaboost Matlab 工具：http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox

    • MultiBoost(不說啥了，多類Adaboost算法的程序)：http://sourceforge.net/projects/multiboost/

    • TextonBoost(我們教研室王冠夫師兄的畢設(shè)):?Jamie Shotton - Code

    • LibSvm的老爹（推薦）:?http://www.csie.ntu.edu.tw/~cjlin/

    • Conditional Random Fields（CRF論文+Code列表，推薦）

    • CRF++: Yet Another CRF toolkit

    • Conditional Random Field (CRF) Toolbox for Matlab

    • Tree CRFs

    • LingPipe: Installation

    • Hidden Markov Models（推薦）

    • 隱馬爾科夫模型(Hidden Markov Models)系列之一?- eaglex的專欄 - 博客頻道?- CSDN.NET（推薦）

      
      
    綜合代碼

    • CvPapers(好吧，牛吧網(wǎng)站，里面有ICCV，CVPR，ECCV，SIGGRAPH的論文收錄，然后還有一些論文的代碼搜集，要求加精！)：http://www.cvpapers.com/

    • Computer Vision Software(里面代碼很多，并詳細的給出了分類)：http://peipa.essex.ac.uk/info/software.html

    • 某人的Windows Live（我看里面東東不少就收藏了）：https://skydrive.live.com/?cid=3b6244088fd5a769#cid=3B6244088FD5A769&id=3B6244088FD5A769!523

    • MATLAB and Octave Functions for Computer Vision and Image Processing（這個里面的東西也很全，只是都是用Matlab和Octave開發(fā)的）：http://www.csse.uwa.edu.au/~pk/research/matlabfns/

    • Computer Vision Resources（里面的視覺算法很多，給出了相應(yīng)的論文和Code，挺好的）：https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

    • MATLAB Functions for Multiple View Geometry（關(guān)于物體多視角計算的庫）：http://www.robots.ox.ac.uk/~vgg/hzbook/code/

    • Evolutive Algorithm based on Na?ve Bayes models Estimation（單獨列了一個算法的Code）：http://www.cvc.uab.cat/~xbaro/eanbe/#_Software

      
      
    主頁代碼

    • Pablo Negri's Home Page

    • Jianxin Wu's homepage
      

    • Peter Carbonetto

    • Markov Random Fields for Super-Resolution

    • Detecting and Sketching the Common

    • Pedro Felzenszwalb

    • Hae JONG, SEO

    • CAP 5416 - Computer Vision

    • Parallel Tracking and Mapping for Small AR Workspaces (PTAM)

    • Deva Ramanan - UC Irvine - Computer Vision

    • Raghuraman Gopalan

    • Hui Kong

    • Jamie Shotton - Post-Doctoral Researcher in Computer Vision

    • Jean-Yves AUDIBERT

    • Olga Veksler

    • Stephen Gould

    • Publications (Last Update: 09/30/10)

    • Karim Ali - FlowBoost

    • A simple parts and structure object detector

    • Code - Oxford Brookes Vision Group

    • Taku Kudo

      
      
    行人檢測

    • Histogram of Oriented Gradient (Windows)

    • INRIA Pedestrian detector

    • Poselets

    • William Robson Schwartz - Softwares

    • calvin upper-body detector v1.02

    • RPT@CVG

    • Main Page

    • Source Code

    • Dr. Luciano Spinello

    • Pedestrian Detection

    • Class-Specific Hough Forests for Object Detection

    • Jianxin Wu's homepage（就是上面的）

    • Berkeley大學(xué)做的Pedestrian Detector，使用交叉核的支持向量機，特征使用HOG金字塔，提供Matlab和C++混編的代碼：http://www.cs.berkeley.edu/~smaji/projects/ped-detector/

      
      
    視覺壁障

    • High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning

    • TLD(2010年很火的tracking算法)

    • online boosting trackers

    • Boris Babenko

    • Optical Flow Algorithm Evaluation (提供了一個動態(tài)貝葉斯網(wǎng)絡(luò)框架，例如遞 歸信息處理與分析、卡爾曼濾波、粒子濾波、序列蒙特卡羅方法等，C++寫的)http://of-eval.sourceforge.net/

      
      
    物體檢測算法

    • Object Detection

    • Software for object detection

      
      
    人臉檢測

    • Source Code

    • 10個人臉檢測項目

    • Jianxin Wu's homepage（又是這貨）

      
      
    ICA獨立成分分析

    • An ICA page-papers,code,demo,links (Tony Bell)

    • FastICA

    • Cached k-d tree search for ICP algorithms

      
      
    濾波算法

    • 卡爾曼濾波：The Kalman Filter(終極網(wǎng)頁)

    • Bayesian Filtering Library:?The Bayesian Filtering Library

      
      
    路面識別

    • Source Code

    • Vanishing point detection for general road detection

      
      
    分割算法

    • MATLAB Normalized Cuts Segmentation Code：software

    • 超像素分割：SLIC Superpixels

      
      

    ZZ：?http://blog.sina.com.cn/s/blog_5086c3e20101kdy5.html，http://www.yuanyong.org/cv/cv-code-three.html

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