计算机视觉中的相关算法的源代码

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、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]

• ClassCut for Unsupervised Class Segmentation[code]

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

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]

• 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、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]

• Saliency Detection: A Spectral Residual Approach[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]

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

八、Kinect：

• Kinect toolbox[Project]

• OpenNI[Project]

• zouxy09 CSDN Blog[Resource]

• FingerTracker 手指跟踪[code]

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]

• 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[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、图像处理：

• Distance Transforms of Sampled Functions[Project]

• The Computer Vision Homepage[Project]

• Efficient appearance distances between windows[code]

• Image Exploration algorithm[code]

• Motion Magnification 运动放大 [Project]

• Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]

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

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]

十四、人手及指尖检测与识别：

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

• Hand and Finger Detection using JavaCV[Project]

• Hand and fingers detection[Code]

十五、场景解释：

• 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、图像检索Image Retrieval：

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

十八、马尔科夫随机场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、运动检测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 also ActionBank

    • 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 link for 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(当然这只是其中之一)

    • LBP特征(一种纹理特征)：http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html

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

    机器视觉

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

    • Boosting(该网页上有相当全的Boosting的文章和几个Boosting代码，本人推荐)：http://cbio.mskcc.org/~aarvey/boosting_papers.html

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

    • MultiBoost(不说啥了，多类Adaboost算法的程序)：http://sourceforge.net/projects/multiboost/

    • TextonBoost(我们教研室王冠夫师兄的毕设): Jamie Shotton - Code

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

    • Conditional Random Fields（CRF论文+Code列表，推荐）

    • CRF++: Yet Another CRF toolkit

    • Conditional Random Field (CRF) Toolbox for Matlab

    • Tree CRFs

    • LingPipe: Installation

    • Hidden Markov Models（推荐）

    • 隐马尔科夫模型(Hidden Markov Models)系列之一 - eaglex的专栏 - 博客频道 - CSDN.NET（推荐）

    综合代码

    • CvPapers(好吧，牛吧网站，里面有ICCV，CVPR，ECCV，SIGGRAPH的论文收录，然后还有一些论文的代码搜集，要求加精！)：http://www.cvpapers.com/

    • Computer Vision Software(里面代码很多，并详细的给出了分类)：http://peipa.essex.ac.uk/info/software.html

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

    • MATLAB and Octave Functions for Computer Vision and Image Processing（这个里面的东西也很全，只是都是用Matlab和Octave开发的）：http://www.csse.uwa.edu.au/~pk/research/matlabfns/

    • Computer Vision Resources（里面的视觉算法很多，给出了相应的论文和Code，挺好的）：https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

    • MATLAB Functions for Multiple View Geometry（关于物体多视角计算的库）：http://www.robots.ox.ac.uk/~vgg/hzbook/code/

    • Evolutive Algorithm based on Naïve Bayes models Estimation（单独列了一个算法的Code）：http://www.cvc.uab.cat/~xbaro/eanbe/#_Software

    主页代码

    • 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

    行人检测

    • 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大学做的Pedestrian Detector，使用交叉核的支持向量机，特征使用HOG金字塔，提供Matlab和C++混编的代码：http://www.cs.berkeley.edu/~smaji/projects/ped-detector/

    视觉壁障

    • 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 (提供了一个动态贝叶斯网络框架，例如递 归信息处理与分析、卡尔曼滤波、粒子滤波、序列蒙特卡罗方法等，C++写的)http://of-eval.sourceforge.net/

    物体检测算法

    • Object Detection

    • Software for object detection

    人脸检测

    • Source Code

    • 10个人脸检测项目

    • Jianxin Wu's homepage（又是这货）

    ICA独立成分分析

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

    • FastICA

    • Cached k-d tree search for ICP algorithms

    滤波算法

    • 卡尔曼滤波：The Kalman Filter(终极网页)

    • Bayesian Filtering Library: The Bayesian Filtering Library

    路面识别

    • 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