计算机视觉常用开源库

浅浅的花香味﹌ 2022-04-04 16:48 485阅读 0赞

转载 [https://blog.csdn.net/MAGIC\_YS/article/details/40918303][https_blog.csdn.net_MAGIC_YS_article_details_40918303]

转载自：http://blog.csdn.net/likaidlut/article/details/18519053

一、特征提取Feature Extraction：

*  SIFT \[1\] \[[Demo program][]\]\[[SIFT Library][]\] \[[VLFeat][]\]
 *  PCA-SIFT \[2\] \[[Project][]\]
 *  Affine-SIFT \[3\] \[[Project][Project 1]\]
 *  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 \[9\] \[[Code][Code 1]\]
 *  Histogram of Oriented Graidents \[10\] \[[INRIA Object Localization Toolkit][]\] \[[OLT toolkit for Windows][]\]
 *  GIST \[11\] \[[Project][Project 2]\]
 *  Shape Context \[12\] \[[Project][Project 3]\]
 *  Color Descriptor \[13\] \[[Project][Project 4]\]
 *  Pyramids of Histograms of Oriented Gradients \[[Code][Code 2]\]
 *  Space-Time Interest Points (STIP) \[14\]\[[Project][Project 5]\] \[[Code][Code 3]\]
 *  Boundary Preserving Dense Local Regions \[15\]\[[Project][Project 6]\]
 *  Weighted Histogram\[[Code][Code 4]\]
 *  Histogram-based Interest Points Detectors\[[Paper][]\]\[[Code][Code 5]\]
 *  An OpenCV - C++ implementation of Local Self Similarity Descriptors \[[Project][Project 7]\]
 *  Fast Sparse Representation with Prototypes\[[Project][Project 8]\]
 *  Corner Detection \[[Project][Project 9]\]
 *  AGAST Corner Detector: faster than FAST and even FAST-ER\[[Project][Project 10]\]
 *  Real-time Facial Feature Detection using Conditional Regression Forests\[[Project][Project 11]\]
 *  Global and Efficient Self-Similarity for Object Classification and Detection\[[code][]\]
 *  WαSH: Weighted α-Shapes for Local Feature Detection\[[Project][Project 12]\]
 *  HOG\[[Project][Project 13]\]
 *  Online Selection of Discriminative Tracking Features\[[Project][Project 14]\]

二、图像分割Image Segmentation：

*  Normalized Cut \[1\] \[[Matlab code][]\]
 *  Gerg Mori’ Superpixel code \[2\] \[[Matlab code][Matlab code 1]\]
 *  Efficient Graph-based Image Segmentation \[3\] \[[C++ code][C_ code]\] \[[Matlab wrapper][]\]
 *  Mean-Shift Image Segmentation \[4\] \[[EDISON C++ code][EDISON C_ code]\] \[[Matlab wrapper][Matlab wrapper 1]\]
 *  OWT-UCM Hierarchical Segmentation \[5\] \[[Resources][]\]
 *  Turbepixels \[6\] \[[Matlab code 32bit][]\] \[[Matlab code 64bit][]\] \[[Updated code][]\]
 *  Quick-Shift \[7\] \[[VLFeat][VLFeat 1]\]
 *  SLIC Superpixels \[8\] \[[Project][Project 15]\]
 *  Segmentation by Minimum Code Length \[9\] \[[Project][Project 16]\]
 *  Biased Normalized Cut \[10\] \[[Project][Project 17]\]
 *  Segmentation Tree \[11-12\] \[[Project][Project 18]\]
 *  Entropy Rate Superpixel Segmentation \[13\] \[[Code][Code 6]\]
 *  Fast Approximate Energy Minimization via Graph Cuts\[[Paper][Paper 1]\]\[[Code][Code 7]\]
 *  Efﬁcient Planar Graph Cuts with Applications in Computer Vision\[[Paper][Paper 2]\]\[[Code][Code 8]\]
 *  Isoperimetric Graph Partitioning for Image Segmentation\[[Paper][Paper 3]\]\[[Code][Code 9]\]
 *  Random Walks for Image Segmentation\[[Paper][Paper 4]\]\[[Code][Code 10]\]
 *  Blossom V: A new implementation of a minimum cost perfect matching algorithm\[[Code][Code 11]\]
 *  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision\[[Paper][Paper 5]\]\[[Code][Code 12]\]
 *  Geodesic Star Convexity for Interactive Image Segmentation\[[Project][Project 19]\]
 *  Contour Detection and Image Segmentation Resources\[[Project][Resources]\]\[[Code][Code 13]\]
 *  Biased Normalized Cuts\[[Project][Project 20]\]
 *  Max-flow/min-cut\[[Project][Project 21]\]
 *  Chan-Vese Segmentation using Level Set\[[Project][Project 22]\]
 *  A Toolbox of Level Set Methods\[[Project][Project 23]\]
 *  Re-initialization Free Level Set Evolution via Reaction Diffusion\[[Project][Project 24]\]
 *  Improved C-V active contour model\[[Paper][Paper 6]\]\[[Code][Code 14]\]
 *  A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction\[[Paper][Paper 7]\]\[[Code][Code 15]\]
 *  Level Set Method Research by Chunming Li\[[Project][Project 25]\]
 *  ClassCut for Unsupervised Class Segmentation\[[cod][]e\]
 *  SEEDS: Superpixels Extracted via Energy-Driven Sampling [\[Project][Project 26]\]\[[other][]\]

三、目标检测Object Detection：

*  A simple object detector with boosting \[[Project][Project 27]\]
 *  INRIA Object Detection and Localization Toolkit \[1\] \[[Project][Project 28]\]
 *  Discriminatively Trained Deformable Part Models \[2\] \[[Project][Project 29]\]
 *  Cascade Object Detection with Deformable Part Models \[3\] \[[Project][Project 30]\]
 *  Poselet \[4\] \[[Project][Project 31]\]
 *  Implicit Shape Model \[5\] \[[Project][Project 32]\]
 *  Viola and Jones’s Face Detection \[6\] \[[Project][Project 33]\]
 *  Bayesian Modelling of Dyanmic Scenes for Object Detection\[[Paper][Paper 8]\]\[[Code][Code 16]\]
 *  Hand detection using multiple proposals\[[Project][Project 34]\]
 *  Color Constancy, Intrinsic Images, and Shape Estimation\[[Paper][Paper 9]\]\[[Code][Code 17]\]
 *  Discriminatively trained deformable part models\[[Project][Project 35]\]
 *  Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD \[[Project][Project 36]\]
 *  Image Processing On Line\[[Project][Project 37]\]
 *  Robust Optical Flow Estimation\[[Project][Project 38]\]
 *  Where's Waldo: Matching People in Images of Crowds\[[Project][Project 39]\]
 *  Scalable Multi-class Object Detection\[[Project][Project 40]\]
 *  Class-Specific Hough Forests for Object Detection\[[Project][Project 41]\]
 *  Deformed Lattice Detection In Real-World Images\[[Project][Project 42]\]
 *  Discriminatively trained deformable part models\[[Project][Project 35]\]

四、显著性检测Saliency Detection：

*  Itti, Koch, and Niebur’ saliency detection \[1\] \[[Matlab code][Matlab code 2]\]
 *  Frequency-tuned salient region detection \[2\] \[[Project][Project 43]\]
 *  Saliency detection using maximum symmetric surround \[3\] \[[Project][Project 44]\]
 *  Attention via Information Maximization \[4\] \[[Matlab code][Matlab code 3]\]
 *  Context-aware saliency detection \[5\] \[[Matlab code][Matlab code 4]\]
 *  Graph-based visual saliency \[6\] \[[Matlab code][Matlab code 5]\]
 *  Saliency detection: A spectral residual approach. \[7\] \[[Matlab code][Matlab code 6]\]
 *  Segmenting salient objects from images and videos. \[8\] \[[Matlab code][Matlab code 7]\]
 *  Saliency Using Natural statistics. \[9\] \[[Matlab code][Matlab code 8]\]
 *  Discriminant Saliency for Visual Recognition from Cluttered Scenes. \[10\] \[[Code][Code 18]\]
 *  Learning to Predict Where Humans Look \[11\] \[[Project][Project 45]\]
 *  Global Contrast based Salient Region Detection \[12\] \[[Project][Project 46]\]
 *  Bayesian Saliency via Low and Mid Level Cues\[[Project][Project 47]\]
 *  Top-Down Visual Saliency via Joint CRF and Dictionary Learning\[[Paper][Paper 10]\]\[[Code][Code 19]\]
 *  Saliency Detection: A Spectral Residual Approach\[[Code][Code 20]\]

五、图像分类、聚类Image Classification, Clustering

*  Pyramid Match \[1\] \[[Project][Project 48]\]
 *  Spatial Pyramid Matching \[2\] \[[Code][Code 21]\]
 *  Locality-constrained Linear Coding \[3\] \[[Project][Project 49]\] \[[Matlab code][Matlab code 9]\]
 *  Sparse Coding \[4\] \[[Project][Project 50]\] \[[Matlab code][Matlab code 10]\]
 *  Texture Classification \[5\] \[[Project][Project 51]\]
 *  Multiple Kernels for Image Classification \[6\] \[[Project][Code]\]
 *  Feature Combination \[7\] \[[Project][Project 52]\]
 *  SuperParsing \[[Code][Code 22]\]
 *  Large Scale Correlation Clustering Optimization\[[Matlab code][Matlab code 11]\]
 *  Detecting and Sketching the Common\[[Project][Project 53]\]
 *  Self-Tuning Spectral Clustering\[[Project][Project 54]\]\[[Code][Code 23]\]
 *  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior\[[Paper][Paper 11]\]\[[Code][Code 24]\]
 *  Filters for Texture Classification\[[Project][Project 55]\]
 *  Multiple Kernel Learning for Image Classification\[[Project][Code]\]
 *  SLIC Superpixels\[[Project][Project 56]\]

六、抠图Image Matting

*  A Closed Form Solution to Natural Image Matting \[[Code][Code 25]\]
 *  Spectral Matting \[[Project][Project 57]\]
 *  Learning-based Matting \[[Code][Code 26]\]

七、目标跟踪Object Tracking：

*  A Forest of Sensors - Tracking Adaptive Background Mixture Models \[[Project][Project 58]\]
 *  Object Tracking via Partial Least Squares Analysis\[[Paper][Paper 12]\]\[[Code][Code 27]\]
 *  Robust Object Tracking with Online Multiple Instance Learning\[[Paper][Paper 13]\]\[[Code][Code 28]\]
 *  Online Visual Tracking with Histograms and Articulating Blocks\[[Project][Project 59]\]
 *  Incremental Learning for Robust Visual Tracking\[[Project][Project 60]\]
 *  Real-time Compressive Tracking\[[Project][Project 61]\]
 *  Robust Object Tracking via Sparsity-based Collaborative Model\[[Project][Project 62]\]
 *  Visual Tracking via Adaptive Structural Local Sparse Appearance Model\[[Project][Project 63]\]
 *  Online Discriminative Object Tracking with Local Sparse Representation\[[Paper][Paper 14]\]\[[Code][Code 29]\]
 *  Superpixel Tracking\[[Project][Project 64]\]
 *  Learning Hierarchical Image Representation with Sparsity, Saliency and Locality\[[Paper][Paper 15]\]\[[Code][Code 30]\]
 *  Online Multiple Support Instance Tracking \[[Paper][Paper 16]\]\[[Code][Code 31]\]
 *  Visual Tracking with Online Multiple Instance Learning\[[Project][Code 28]\]
 *  Object detection and recognition\[[Project][Project 65]\]
 *  Compressive Sensing Resources\[[Project][Project 66]\]
 *  Robust Real-Time Visual Tracking using Pixel-Wise Posteriors\[[Project][Project 67]\]
 *  Tracking-Learning-Detection\[[Project][Project 68]\]\[[OpenTLD/C++ Code][OpenTLD_C_ Code]\]
 *  the HandVu：vision-based hand gesture interface\[[Project][Project 69]\]
 *  Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities\[[Project][Project 70]\]

八、Kinect：

*  Kinect toolbox\[[Project][Project 71]\]
 *  OpenNI\[[Project][Project 72]\]
 *  zouxy09 CSDN Blog\[[Resource][]\]
 *  FingerTracker 手指跟踪\[[code][code 1]\]

九、3D相关：

*  3D Reconstruction of a Moving Object\[[Paper][Paper 17]\] \[[Code][Code 32]\]
 *  Shape From Shading Using Linear Approximation\[[Code][Code 33]\]
 *  Combining Shape from Shading and Stereo Depth Maps\[[Project][Project 73]\]\[[Code][Code 34]\]
 *  Shape from Shading: A Survey\[[Paper][Paper 18]\]\[[Code][Code 35]\]
 *  A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)\[[Project][Project 74]\]\[[Code][Code 36]\]
 *  Multi-camera Scene Reconstruction via Graph Cuts\[[Paper][Paper 19]\]\[[Code][Code 37]\]
 *  A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination\[[Paper][Paper 20]\]\[[Code][Code 38]\]
 *  Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture\[[Project][Project 75]\]
 *  Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers\[[Code][Code 39]\]
 *  Learning 3-D Scene Structure from a Single Still Image\[[Project][Project 76]\]

十、机器学习算法：

*  Matlab class for computing Approximate Nearest Nieghbor (ANN) \[[Matlab class][] providing interface to[ANN library][]\]
 *  Random Sampling\[[code][code 2]\]
 *  Probabilistic Latent Semantic Analysis (pLSA)\[[Code][Code 40]\]
 *  FASTANN and FASTCLUSTER for approximate k-means (AKM)\[[Project][Project 77]\]
 *  Fast Intersection / Additive Kernel SVMs\[[Project][Project 78]\]
 *  SVM\[[Code][Code 41]\]
 *  Ensemble learning\[[Project][Project 65]\]
 *  Deep Learning\[[Net][]\]
 *  Deep Learning Methods for Vision\[[Project][Project 79]\]
 *  Neural Network for Recognition of Handwritten Digits\[[Project][Project 80]\]
 *  Training a deep autoencoder or a classifier on MNIST digits\[[Project][Project 81]\]
 *  THE MNIST DATABASE of handwritten digits\[[Project][Project 82]\]
 *  Ersatz：deep neural networks in the cloud\[[Project][Project 83]\]
 *  Deep Learning \[[Project][Project 84]\]
 *  sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++\[[Project][Project 85]\]
 *  Weka 3: Data Mining Software in Java\[[Project][Project 86]\]
 *  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\[[Project][Project 87]\]
 *  Training a deep autoencoder or a classifier on MNIST digits\[[Project][Project 81]\]
 *  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)\[[Code][Code 42]\]
 *  Sparse coding simulation software\[[Project][Project 88]\]
 *  Visual Recognition and Machine Learning Summer School\[[Software][]\]

十一、目标、行为识别Object, Action Recognition：

*  Action Recognition by Dense Trajectories\[[Project][Project 89]\]\[[Code][Code 43]\]
 *  Action Recognition Using a Distributed Representation of Pose and Appearance\[[Project][Project 90]\]
 *  Recognition Using Regions\[[Paper][Paper 21]\]\[[Code][Code 44]\]
 *  2D Articulated Human Pose Estimation\[[Project][Project 91]\]
 *  Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression\[[Paper][Paper 22]\]\[[Code][Code 45]\]
 *  Estimating Human Pose from Occluded Images\[[Paper][Paper 23]\]\[[Code][Code 46]\]
 *  Quasi-dense wide baseline matching\[[Project][Project 92]\]
 *  ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms\[[Project][Project 93]\]
 *  Real Time Head Pose Estimation with Random Regression Forests\[[Project][Project 94]\]
 *  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\[[Project][Project 95]\]
 *  Learning and detecting shape models \[[code][code 3]\]
 *  Progressive Search Space Reduction for Human Pose Estimation\[[Project][Project 96]\]
 *  Learning Non-Rigid 3D Shape from 2D Motion\[[Project][Project 97]\]

十二、图像处理：

*  Distance Transforms of Sampled Functions\[[Project][Project 98]\]
 *  The Computer Vision Homepage\[[Project][Project 99]\]
 *  Efficient appearance distances between windows\[[code][code 4]\]
 *  Image Exploration algorithm\[[code][code 5]\]
 *  Motion Magnification 运动放大 \[[Project][Project 100]\]
 *  Bilateral Filtering for Gray and Color Images 双边滤波器 \[[Project][Project 101]\]
 *  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\[[Project][Project 102]\] \[[Code][Code 47]\]
 *  a development kit of matlab mex functions for OpenCV library\[[Project][Project 103]\]
 *  Fast Artificial Neural Network Library\[[Project][Project 104]\]

十四、人手及指尖检测与识别：

*  finger-detection-and-gesture-recognition \[[Code][Code 48]\]
 *  Hand and Finger Detection using JavaCV\[[Project][Project 105]\]
 *  Hand and fingers detection\[[Code][Code 49]\]

十五、场景解释：

*  Nonparametric Scene Parsing via Label Transfer \[[Project][Project 106]\]

十六、光流Optical flow：

*  High accuracy optical flow using a theory for warping \[[Project][Project 107]\]
 *  Dense Trajectories Video Description \[[Project][Project 89]\]
 *  SIFT Flow: Dense Correspondence across Scenes and its Applications\[[Project][Project 108]\]
 *  KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker \[[Project][Project 109]\]
 *  Tracking Cars Using Optical Flow\[[Project][Project 110]\]
 *  Secrets of optical flow estimation and their principles\[[Project][Project 111]\]
 *  implmentation of the Black and Anandan dense optical flow method\[[Project][Project 111]\]
 *  Optical Flow Computation\[[Project][Project 112]\]
 *  Beyond Pixels: Exploring New Representations and Applications for Motion Analysis\[[Project][Project 113]\]
 *  A Database and Evaluation Methodology for Optical Flow\[[Project][Project 114]\]
 *  optical flow relative\[[Project][Project 115]\]
 *  Robust Optical Flow Estimation \[[Project][Project 38]\]
 *  optical flow\[[Project][Project 116]\]

十七、图像检索Image Retrieval：

*  Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [\[Paper][Paper 24]\]\[[code][code 6]\]

十八、马尔科夫随机场Markov Random Fields：

*  Markov Random Fields for Super-Resolution [\[][Paper 24][Project][Project 117]\]
 *  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors \[[Project][Project 118]\]

十九、运动检测Motion detection：

*  Moving Object Extraction, Using Models or Analysis of Regions [\[][Paper 24][Project][Project 119]\]
 *  Background Subtraction: Experiments and Improvements for ViBe \[[Project][Project 120]\]
 *  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications \[[Project][Project 121]\]
 *  changedetection.net: A new change detection benchmark dataset\[[Project][Project 122]\]
 *  ViBe - a powerful technique for background detection and subtraction in video sequences\[[Project][Project 123]\]
 *  Background Subtraction Program\[[Project][Project 124]\]
 *  Motion Detection Algorithms\[[Project][Project 125]\]
 *  Stuttgart Artificial Background Subtraction Dataset\[[Project][Project 126]\]
 *  Object Detection, Motion Estimation, and Tracking\[[Project][Project 127]\]
    
    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][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][Project 10] – 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][OpenCV 1], [Original code][] by David Lowe, [GPU implementation][], [OpenSIFT][]
     *  SURF: [Herbert Bay’s code][Herbert Bay_s code], [OpenCV][OpenCV 1], [GPU-SURF][]
    
    Other Local Feature Detectors and Descriptors:
    
     *  [VGG Affine Covariant features][Oxford project] – 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][Project 2] – 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][Project 35] – 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][Project 31] – 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][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][OpenCV 2] – 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.
         *  \[url=http://vis-[www.cs.umass.edu/lfw/][www.cs.umass.edu_lfw]\]LFW\[/url\] – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
         *  [Human Attributes][Project 31] – 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][link 1] 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
        
         *  \[url=http://vis-[www.cs.umass.edu/fddb/][www.cs.umass.edu_fddb]\]FDDB\[/url\] – UMass face detection dataset and benchmark (5,000+ faces)
         *  [CMU/MIT][CMU_MIT] – Classical face detection dataset.
        
        Face Recognition
        
         *  [Face Recognition Homepage][] – Large collection of face recognition datasets.
         *  \[url=http://vis-[www.cs.umass.edu/lfw/][www.cs.umass.edu_lfw]\]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][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][Project 82] – 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][PASCAL Human Detection] – 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. Check[VLBenchmarks][]for 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][http_rogerioferis.com_VisualRecognitionAndSearch_Resources.html]
        
          
        特征提取
        
         *  SURF特征: [http://www.vision.ee.ethz.ch/software/index.de.html][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][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][Project 27]
         *  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][http_cbio.mskcc.org_aarvey_boosting_papers.html]
         *  Adaboost Matlab 工具：[http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox][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/][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/][http_www.csie.ntu.edu.tw_cjlin]
         *  [Conditional Random Fields][]（CRF论文+Code列表，推荐）
         *  [CRF++: Yet Another CRF toolkit][CRF_ Yet Another CRF toolkit]
         *  [Conditional Random Field (CRF) Toolbox for Matlab][Conditional Random Field _CRF_ Toolbox for Matlab]
         *  [Tree CRFs][]
         *  [LingPipe: Installation][LingPipe_ Installation]
         *  [Hidden Markov Models][]（推荐）
         *  [隐马尔科夫模型][Link 1][(Hidden Markov Models)][Link 1][系列之一][Link 1][ \- eaglex][Link 1][的专栏 - 博客频道 ][Link 1][\- CSDN.NET][Link 1][（推荐）][Link 1]
        
        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/][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][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][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/][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][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/][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][http_www.cvc.uab.cat_xbaro_eanbe_Software]
        
        主页代码
        
         *  [Pablo Negri's Home Page][Pablo Negri_s Home Page]
         *  [Jianxin Wu's homepage][Jianxin Wu_s homepage]
         *  [Peter Carbonetto][]
         *  [Markov Random Fields for Super-Resolution][Project 117]
         *  [Detecting and Sketching the Common][]
         *  [Pedro Felzenszwalb][]
         *  [Hae JONG, SEO][Hae JONG_ SEO]
         *  [CAP 5416 - Computer Vision][]
         *  [Parallel Tracking and Mapping for Small AR Workspaces (PTAM)][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)][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)][OLT toolkit for Windows]
         *  [INRIA Pedestrian detector][]
         *  [Poselets][Project 31]
         *  [William Robson Schwartz - Softwares][]
         *  [calvin upper-body detector v1.02][]
         *  [RPT@CVG][RPT_CVG]
         *  [Main Page][]
         *  [Source Code][]
         *  [Dr. Luciano Spinello][]
         *  [Pedestrian Detection][]
         *  [Class-Specific Hough Forests for Object Detection][]
         *  [Jianxin Wu's homepage][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/][INRIA Pedestrian 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][Code 28]
         *  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][10]
         *  [Jianxin Wu's homepage][Jianxin Wu_s homepage]（又是这货）
        
        ICA独立成分分析
        
         *  [An ICA page-papers,code,demo,links (Tony Bell)][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][Source Code 1]
         *  [Vanishing point detection for general road detection][]
        
        分割算法
        
         *  MATLAB Normalized Cuts Segmentation Code：[software][Matlab code]
         *  超像素分割：[SLIC Superpixels][]

[https_blog.csdn.net_MAGIC_YS_article_details_40918303]: https://blog.csdn.net/MAGIC_YS/article/details/40918303
[Demo program]: http://www.cs.ubc.ca/~lowe/keypoints/siftDemoV4.zip
[SIFT Library]: http://blogs.oregonstate.edu/hess/code/sift/
[VLFeat]: http://www.vlfeat.org/
[Project]: http://www.cs.cmu.edu/~yke/pcasift/
[Project 1]: http://www.ipol.im/pub/algo/my_affine_sift/
[OpenSURF]: http://www.chrisevansdev.com/computer-vision-opensurf.html
[Matlab Wrapper]: http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php
[Oxford project]: http://www.robots.ox.ac.uk/~vgg/research/affine/
[Code]: http://www.robots.ox.ac.uk/~vgg/software/MKL/
[Oxford implementation]: http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/
[Code 1]: http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz
[INRIA Object Localization Toolkit]: http://www.navneetdalal.com/software
[OLT toolkit for Windows]: http://www.computing.edu.au/~12482661/hog.html
[Project 2]: http://people.csail.mit.edu/torralba/code/spatialenvelope/
[Project 3]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html
[Project 4]: http://koen.me/research/colordescriptors/
[Code 2]: http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip
[Project 5]: http://www.nada.kth.se/cvap/abstracts/cvap284.html
[Code 3]: http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip
[Project 6]: http://vision.cs.utexas.edu/projects/bplr/bplr.html
[Code 4]: http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/whistc.tar.gz
[Paper]: http://www.cs.nthu.edu.tw/~htchen/hipd_cvpr09.pdf
[Code 5]: http://740-2.cs.nthu.edu.tw/~htchen/hipd/hist_corner.zip
[Project 7]: http://intuitionlogic.com/post/2011/04/11/A-OpenCV-C++-implementation-of-Local-Self-Similarity-Descriptors.aspx
[Project 8]: http://faculty.ucmerced.edu/mhyang/cvpr10_fsr.html
[Project 9]: http://kiwi.cs.dal.ca/~dparks/CornerDetection/index.htm
[Project 10]: http://www6.in.tum.de/Main/ResearchAgast
[Project 11]: http://files.is.tue.mpg.de/jgall/projects/facialfeatures/facialfeatures.html
[code]: http://groups.inf.ed.ac.uk/calvin/gss/selfsim_release1.0.tgz
[Project 12]: http://image.ntua.gr/iva/research/wash/
[Project 13]: http://soc.fudan.edu.cn/vip/projects/gradproj/wiki/HOG%E4%BB%A3%E7%A0%81
[Project 14]: http://www.cs.ucla.edu/~roozbehm/cs7495/report.html
[Matlab code]: http://www.cis.upenn.edu/~jshi/software/
[Matlab code 1]: http://www.cs.sfu.ca/~mori/research/superpixels/
[C_ code]: http://people.cs.uchicago.edu/~pff/segment/
[Matlab wrapper]: http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation
[EDISON C_ code]: http://coewww.rutgers.edu/riul/research/code/EDISON/index.html
[Matlab wrapper 1]: http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz
[Resources]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
[Matlab code 32bit]: http://www.cs.toronto.edu/~babalex/turbopixels_code.tar.gz
[Matlab code 64bit]: http://www.cs.toronto.edu/~babalex/TurboPixels64.rar
[Updated code]: http://www.cs.toronto.edu/~babalex/superpixels_update.tgz
[VLFeat 1]: http://www.vlfeat.org/overview/quickshift.html
[Project 15]: http://ivrgwww.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html
[Project 16]: http://perception.csl.uiuc.edu/coding/image_segmentation/
[Project 17]: http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/
[Project 18]: http://vision.ai.uiuc.edu/segmentation
[Code 6]: http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip
[Paper 1]: http://www.csd.uwo.ca/faculty/olga/Papers/pami01_final.pdf
[Code 7]: http://vision.csd.uwo.ca/code/gco-v3.0.zip
[Paper 2]: http://www.csd.uwo.ca/~schmidtf/pdf/schmidt_et_al_cvpr09.pdf
[Code 8]: http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip
[Paper 3]: http://www.cns.bu.edu/~lgrady/grady2006isoperimetric.pdf
[Code 9]: http://www.cns.bu.edu/~lgrady/grady2006isoperimetric_code.zip
[Paper 4]: http://www.cns.bu.edu/~lgrady/grady2006random.pdf
[Code 10]: http://www.cns.bu.edu/~lgrady/random_walker_matlab_code.zip
[Code 11]: http://pub.ist.ac.at/~vnk/software/blossom5-v2.03.src.tar.gz%20%20http:/pub.ist.ac.at/~vnk/software/blossom5-v2.03.src.tar.gz
[Paper 5]: http://www.csd.uwo.ca/~yuri/Papers/pami04.pdf
[Code 12]: http://pub.ist.ac.at/~vnk/software/maxflow-v3.01.src.tar.gz
[Project 19]: http://www.robots.ox.ac.uk/~vgg/software/iseg/
[Code 13]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_source.tgz
[Project 20]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/biasedNcuts/
[Project 21]: http://vision.csd.uwo.ca/code/
[Project 22]: http://www.ipol.im/pub/art/2012/g-cv/
[Project 23]: http://www.cs.ubc.ca/~mitchell/ToolboxLS/index.html
[Project 24]: http://www4.comp.polyu.edu.hk/~cslzhang/RD/RD.htm
[Paper 6]: http://www4.comp.polyu.edu.hk/~cskhzhang/J_papers/ICV.pdf
[Code 14]: http://www4.comp.polyu.edu.hk/~cskhzhang/J_papers/ICV.rar
[Paper 7]: http://www4.comp.polyu.edu.hk/~cskhzhang/J_papers/ICIP10_SVMLS.pdf
[Code 15]: http://www4.comp.polyu.edu.hk/~cskhzhang/J_papers/SVMLS_v0.rar
[Project 25]: http://www.engr.uconn.edu/~cmli/
[cod]: http://groups.inf.ed.ac.uk/calvin/classcut/ClassCut-release_v1.0.zip
[Project 26]: http://www.vision.ee.ethz.ch/~vamichae/seeds/
[other]: http://www.mvdblive.org/seeds/
[Project 27]: http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
[Project 28]: http://pascal.inrialpes.fr/soft/olt/
[Project 29]: http://people.cs.uchicago.edu/~pff/latent/
[Project 30]: http://people.cs.uchicago.edu/~rbg/star-cascade/
[Project 31]: http://www.eecs.berkeley.edu/~lbourdev/poselets/
[Project 32]: http://www.vision.ee.ethz.ch/~bleibe/code/ism.html
[Project 33]: http://pr.willowgarage.com/wiki/Face_detection
[Paper 8]: http://vision.eecs.ucf.edu/papers/01512057.pdf
[Code 16]: http://vision.eecs.ucf.edu/Code/Background.zip
[Project 34]: http://www.robots.ox.ac.uk/~vgg/software/hands/
[Paper 9]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/reconstruction/BarronMalikECCV2012.pdf
[Code 17]: http://www.cs.berkeley.edu/~barron/BarronMalikECCV2012_code.zip
[Project 35]: http://people.cs.uchicago.edu/~rbg/latent/
[Project 36]: http://campar.cs.tum.edu/Main/StefanHinterstoisser
[Project 37]: http://www.ipol.im/
[Project 38]: http://www.ipol.im/pub/pre/21/
[Project 39]: http://homes.cs.washington.edu/~rahul/data/WheresWaldo.html
[Project 40]: http://files.is.tue.mpg.de/jgall/projects/houghMC/houghMC.html
[Project 41]: http://files.is.tue.mpg.de/jgall/projects/houghforest/houghforest.html
[Project 42]: http://vision.cse.psu.edu/data/data.shtml
[Matlab code 2]: http://www.saliencytoolbox.net/
[Project 43]: http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html
[Project 44]: http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html
[Matlab code 3]: http://www.cse.yorku.ca/~neil/AIM.zip
[Matlab code 4]: http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html
[Matlab code 5]: http://www.klab.caltech.edu/~harel/share/gbvs.php
[Matlab code 6]: http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html
[Matlab code 7]: http://www.cse.oulu.fi/MVG/Downloads/saliency
[Matlab code 8]: http://cseweb.ucsd.edu/~l6zhang/
[Code 18]: http://www.svcl.ucsd.edu/projects/saliency/
[Project 45]: http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html
[Project 46]: http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/
[Project 47]: http://ice.dlut.edu.cn/lu/Project/TIP_scm/TIP_scm.htm
[Paper 10]: http://faculty.ucmerced.edu/mhyang/papers/cvpr12a.pdf
[Code 19]: http://faculty.ucmerced.edu/mhyang/code/top-down-saliency.zip
[Code 20]: http://www.klab.caltech.edu/~xhou/projects/dva/dva.html
[Project 48]: http://people.csail.mit.edu/jjl/libpmk/
[Code 21]: http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip
[Project 49]: http://www.ifp.illinois.edu/~jyang29/LLC.htm
[Matlab code 9]: http://www.ifp.illinois.edu/~jyang29/codes/CVPR10-LLC.rar
[Project 50]: http://www.ifp.illinois.edu/~jyang29/ScSPM.htm
[Matlab code 10]: http://www.ifp.illinois.edu/~jyang29/codes/CVPR09-ScSPM.rar
[Project 51]: http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html
[Project 52]: http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html
[Code 22]: http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip
[Matlab code 11]: http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/LargeScaleCC1.0.tar.gz
[Project 53]: http://www.wisdom.weizmann.ac.il/~vision/SketchTheCommon
[Project 54]: http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html
[Code 23]: http://www.vision.caltech.edu/lihi/Demos/SelfTuning/ZPclustering.zip
[Paper 11]: http://www.wisdom.weizmann.ac.il/~levina/papers/assisted-eccv04.pdf
[Code 24]: http://www.wisdom.weizmann.ac.il/~levina/papers/reflections.zip
[Project 55]: http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html#download
[Project 56]: http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/
[Code 25]: http://people.csail.mit.edu/alevin/matting.tar.gz
[Project 57]: http://www.vision.huji.ac.il/SpectralMatting/
[Code 26]: http://www.mathworks.com/matlabcentral/fileexchange/31412
[Project 58]: http://www.ai.mit.edu/projects/vsam/Tracking/index.html
[Paper 12]: http://faculty.ucmerced.edu/mhyang/papers/tip12_pls_tracking.pdf
[Code 27]: http://faculty.ucmerced.edu/mhyang/code/PLS_tracker_tip.zip
[Paper 13]: http://faculty.ucmerced.edu/mhyang/papers/pami11b.pdf
[Code 28]: http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
[Project 59]: http://www.cise.ufl.edu/~smshahed/tracking.htm
[Project 60]: http://www.cs.toronto.edu/~dross/ivt/
[Project 61]: http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm
[Project 62]: http://faculty.ucmerced.edu/mhyang/project/cvpr12_scm.htm
[Project 63]: http://faculty.ucmerced.edu/mhyang/project/cvpr12_jia_project.htm
[Paper 14]: http://faculty.ucmerced.edu/mhyang/papers/wacv12a.pdf
[Code 29]: http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip
[Project 64]: http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html
[Paper 15]: http://faculty.ucmerced.edu/mhyang/papers/bmvc11a.pdf
[Code 30]: http://faculty.ucmerced.edu/mhyang/code/BMVC11-HSSL-package.zip
[Paper 16]: http://faculty.ucmerced.edu/mhyang/papers/fg11a.pdf
[Code 31]: http://faculty.ucmerced.edu/mhyang/code/fg11_omsit.zip
[Project 65]: http://c2inet.sce.ntu.edu.sg/Jianxin/
[Project 66]: http://dsp.rice.edu/cs
[Project 67]: http://www.robots.ox.ac.uk/~cbibby/index.shtml
[Project 68]: http://info.ee.surrey.ac.uk/Personal/Z.Kalal/
[OpenTLD_C_ Code]: https://github.com/arthurv/OpenTLD
[Project 69]: http://ilab.cs.ucsb.edu/index.php/component/content/article/12/29
[Project 70]: http://files.is.tue.mpg.de/jgall/projects/stochGPLVM/stochGPLVM.html
[Project 71]: http://kinecttoolbox.codeplex.com/
[Project 72]: http://www.openni.org/
[Resource]: http://blog.csdn.net/zouxy09/article/details/8145688
[code 1]: http://makematics.com/code/FingerTracker/
[Paper 17]: http://www.wisdom.weizmann.ac.il/~ronen/papers/Simakov%20Frolova%20Basri%20-%20Dense%20Shape%20Reconstruction%20Under%20Arbitrary%20Unknown%20Lighting.pdf
[Code 32]: http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SFB_matlab1.0.tar.gz
[Code 33]: http://vision.eecs.ucf.edu/shadsrc.html
[Project 73]: http://vision.eecs.ucf.edu/combsrc.html
[Code 34]: http://vision.eecs.ucf.edu/projects/ShapeFromShading/combine.tar.Z
[Paper 18]: http://vision.eecs.ucf.edu/papers/shah/99/ZTCS99.pdf
[Code 35]: http://vision.eecs.ucf.edu/projects/ShapeFromShading/SFS_Survey_1_00.tar.gz
[Project 74]: http://lear.inrialpes.fr/people/klaeser/research_hog3d
[Code 36]: http://lear.inrialpes.fr/people/klaeser/software_3d_video_descriptor
[Paper 19]: http://www.cs.cornell.edu/~rdz/papers/kz-eccv02-recon.pdf
[Code 37]: http://pub.ist.ac.at/~vnk/software/match-v3.4.src.tar.gz
[Paper 20]: http://www.cs.ucf.edu/~vision
[Code 38]: http://www.ee.cityu.edu.hk/~syyuen/Public/SfS/PRL_Perspective_FMM.zip
[Project 75]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/reconstruction/
[Code 39]: http://faculty.ucmerced.edu/mhyang/code/PackagedTrackingCode.tar.gz
[Project 76]: http://ai.stanford.edu/~asaxena/reconstruction3d/
[Matlab class]: http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/ann_wrapper_Mar2012.tar.gz
[ANN library]: http://www.cs.umd.edu/~mount/ANN/
[code 2]: http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/weight_sample.tar.gz
[Code 40]: http://www.robots.ox.ac.uk/~vgg/software/pLSA/pLSA_demo.tgz
[Project 77]: http://www.robots.ox.ac.uk/~vgg/software/fastann/
[Project 78]: http://www.cs.berkeley.edu/~smaji/projects/fiksvm/
[Code 41]: http://osmot.cs.cornell.edu/svm_light/
[Net]: http://deeplearning.net/
[Project 79]: http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/
[Project 80]: http://www.codeproject.com/KB/library/NeuralNetRecognition.aspx
[Project 81]: http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html
[Project 82]: http://yann.lecun.com/exdb/mnist/
[Project 83]: http://www.ersatz1.com/
[Project 84]: http://www.cs.nyu.edu/~yann/research/deep/
[Project 85]: http://www.ics.forth.gr/~lourakis/sparseLM/
[Project 86]: http://www.cs.waikato.ac.nz/ml/weka/
[Video]: http://vipl.ict.ac.cn/News/academic-report-tutorial-deep-learning-dr-kai-yu
[Matlab Tool]: http://www.mathworks.cn/matlabcentral/fileexchange/24291
[Wedsite]: http://yann.lecun.com/exdb/publis/index.html#lecun-98
[Project 87]: http://yann.lecun.com/exdb/lenet/index.html
[Website]: http://www.cs.toronto.edu/~hinton/
[Code 42]: http://lear.inrialpes.fr/people/guillaumin/code.php#mildml
[Project 88]: http://redwood.berkeley.edu/bruno/sparsenet/
[Software]: http://lear.inrialpes.fr/software
[Project 89]: http://lear.inrialpes.fr/people/wang/dense_trajectories
[Code 43]: http://lear.inrialpes.fr/people/wang/download/dense_trajectory_release.tar.gz
[Project 90]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/action/
[Paper 21]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/glam-cvpr09.pdf
[Code 44]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/glam_cvpr09_v2.zip
[Project 91]: http://www.vision.ee.ethz.ch/~calvin/articulated_human_pose_estimation_code/
[Paper 22]: http://faculty.ucmerced.edu/mhyang/papers/cvpr07a.pdf
[Code 45]: http://www.cise.ufl.edu/~smshahed/cvpr07_fast_human_pose.zip
[Paper 23]: http://faculty.ucmerced.edu/mhyang/papers/accv09a.pdf
[Code 46]: http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip
[Project 92]: http://www.ee.oulu.fi/~jkannala/quasidense/quasidense.html
[Project 93]: http://gesture.chalearn.org/data/sample-code
[Project 94]: http://files.is.tue.mpg.de/jgall/projects/RFhead/RFhead.html
[Project 95]: http://groups.inf.ed.ac.uk/calvin/articulated_human_pose_estimation_code/
[code 3]: http://groups.inf.ed.ac.uk/calvin/release-learn-shapes-v1.3.tgz
[Project 96]: http://www.robots.ox.ac.uk/~vgg/software/UpperBody/index.html
[Project 97]: http://movement.stanford.edu/learning-nr-shape/
[Project 98]: http://cs.brown.edu/~pff/dt/
[Project 99]: http://www.cs.cmu.edu/~cil/vision.html
[code 4]: http://groups.inf.ed.ac.uk/calvin/efficientAppDistances/releaseEfficientAppDistances.zip
[code 5]: http://groups.inf.ed.ac.uk/calvin/ReleasedCode/image_exploration_v1.1.tgz
[Project 100]: http://people.csail.mit.edu/celiu/motionmag/motionmag.html
[Project 101]: http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
[Project 102]: http://egt.dii.unisi.it/
[Code 47]: http://egt.dii.unisi.it/download/EGT_v1p3.zip
[Project 103]: http://www.cs.stonybrook.edu/~kyamagu/mexopencv/
[Project 104]: http://leenissen.dk/fann/wp/
[Code 48]: http://code.google.com/p/finger-detection-and-gesture-recognition/downloads/list
[Project 105]: http://www.javacodegeeks.com/2012/12/hand-and-finger-detection-using-javacv.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+JavaCodeGeeks+%28Java+Code+Geeks%29
[Code 49]: http://forum.openframeworks.cc/index.php?topic=1916.0
[Project 106]: http://people.csail.mit.edu/celiu/LabelTransfer/code.html
[Project 107]: http://perception.inrialpes.fr/~chari/myweb/Software/
[Project 108]: http://people.csail.mit.edu/celiu/SIFTflow/
[Project 109]: http://www.ces.clemson.edu/~stb/klt/
[Project 110]: http://www.mathworks.cn/cn/help/vision/examples/tracking-cars-using-optical-flow.html
[Project 111]: http://ps.is.tue.mpg.de/person/black#tabs-code
[Project 112]: https://www.ceremade.dauphine.fr/~peyre/numerical-tour/tours/multidim_5_opticalflow/#37
[Project 113]: http://people.csail.mit.edu/celiu/OpticalFlow/
[Project 114]: http://vision.middlebury.edu/flow/
[Project 115]: http://lmb.informatik.uni-freiburg.de/resources/software.php
[Project 116]: http://www.jonathanmugan.com/GraphicsProject/OpticalFlow/
[Paper 24]: http://www.ee.columbia.edu/~wliu/CVPR08_ssml.pdf
[code 6]: http://www.ee.columbia.edu/~wliu/SSMetric.zip
[Project 117]: http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html
[Project 118]: http://vision.middlebury.edu/MRF/
[Project 119]: http://www.visionbib.com/bibliography/motion-i763.html
[Project 120]: http://www2.ulg.ac.be/telecom/publi/publications/mvd/VanDroogenbroeck2012Background/index.html
[Project 121]: http://www.na.icar.cnr.it/~maddalena.l/MODLab/SoftwareSOBS.html
[Project 122]: http://www.changedetection.net/
[Project 123]: http://www2.ulg.ac.be/telecom/research/vibe/
[Project 124]: http://www.umiacs.umd.edu/~knkim/UMD-BGS/index.html
[Project 125]: http://www.codeproject.com/Articles/10248/Motion-Detection-Algorithms
[Project 126]: http://www.vis.uni-stuttgart.de/index.php?id=sabs
[Project 127]: http://www.mathworks.cn/cn/help/vision/motion-analysis-and-tracking.html
[Modern features_ Software]: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxlY2N2MTJmZWF0dXJlc3xneDo3ZDllMzVhMDA4YzEzNmU2
[VLFeat hands-on session training]: https://sites.google.com/site/eccv12features/
[OpenCV]: http://opencv.org/
[FAST]: http://www.edwardrosten.com/work/fast.html
[BRIEF]: http://cvlab.epfl.ch/software/brief/
[ORB]: http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html
[BRISK]: http://www.asl.ethz.ch/people/lestefan/personal/BRISK
[FREAK]: http://www.ivpe.com/freak.htm
[OpenCV 1]: http://docs.opencv.org/modules/nonfree/doc/feature_detection.html
[Original code]: http://www.cs.ubc.ca/~lowe/keypoints/
[GPU implementation]: http://cs.unc.edu/~ccwu/siftgpu/
[OpenSIFT]: http://robwhess.github.com/opensift/
[Herbert Bay_s code]: http://www.vision.ee.ethz.ch/~surf/
[GPU-SURF]: http://www.visual-experiments.com/demos/gpusurf/
[LIOP descriptor]: http://vision.ia.ac.cn/Students/wzh/publication/liop/index.html
[Local Symmetry Features]: http://www.cs.cornell.edu/projects/symfeat/
[CENTRIST]: https://sites.google.com/site/wujx2001/home
[VGG Feature Encoding Toolkit]: http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit/
[Spatial Pyramid Matching]: http://www.cs.illinois.edu/homes/slazebni/
[EBLearn]: http://eblearn.sourceforge.net/
[Torch7]: http://www.torch.ch/
[Deep Learning]: http://deeplearning.net/software_links/
[Efficient Deformable Part-Based Detector]: http://vision.mas.ecp.fr/Personnel/iasonas/dpms.html
[Accelerated Deformable Part Model]: http://www.idiap.ch/~cdubout/coding.html
[Coarse-to-Fine Deformable Part Model]: http://iselab.cvc.uab.es/CoarseToFine
[Part-based Face Detector and Pose Estimation]: http://www.ics.uci.edu/~xzhu/face/
[Relative Attributes]: http://ttic.uchicago.edu/~dparikh/relative.html#code
[Object Bank]: http://vision.stanford.edu/projects/objectbank/
[ActionBank]: http://www.cse.buffalo.edu/~jcorso/r/actionbank/
[Classemes_ Picodes_ and Meta-class features]: http://vlg.cs.dartmouth.edu/projects/vlg_extractor/vlg_extractor/Home.html
[Additive Kernels]: http://ttic.uchicago.edu/~smaji/projects/fiksvm/
[LIBLINEAR]: http://www.csie.ntu.edu.tw/~cjlin/liblinear/
[FLANN]: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
[Kernelized LSH]: http://www.cse.ohio-state.edu/~kulis/klsh/klsh.htm
[ITQ Binary codes]: http://www.unc.edu/~yunchao/itq.htm
[INRIA Image Retrieval]: http://lear.inrialpes.fr/src/inria_fisher/
[Part-based Models]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html#parts
[Convolutional Nets]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html#convnets
[Pedestrian Detection at 100fps]: https://bitbucket.org/rodrigob/doppia
[Caltech Pedestrian Detection Benchmark]: http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
[OpenCV 2]: http://docs.opencv.org/trunk/modules/objdetect/doc/cascade_classification.html?highlight=face%20detection
[Efficient Subwindow Search]: https://sites.google.com/site/christophlampert/software
[Point-Cloud Library]: http://www.pointclouds.org/
[STIP Features]: http://www.di.ens.fr/~laptev/download.html
[Independent Subspace Analysis]: http://ai.stanford.edu/~quocle/
[Velocity Histories of Tracked Keypoints]: http://www.cs.rochester.edu/~rmessing/uradl/
[Animals with Attributes]: http://attributes.kyb.tuebingen.mpg.de/
[aYahoo and aPascal]: http://vision.cs.uiuc.edu/attributes/
[FaceTracer]: http://www.cs.columbia.edu/CAVE/databases/facetracer/
[PubFig]: http://www.cs.columbia.edu/CAVE/databases/pubfig/
[www.cs.umass.edu_lfw]: http://www.cs.umass.edu/lfw/
[link]: https://sharma.users.greyc.fr/hatdb/
[SUN Attribute Database]: http://cs.brown.edu/~gen/sunattributes.html
[ImageNet Attributes]: http://www.image-net.org/download-attributes
[Relative attributes]: http://ttic.uchicago.edu/~dparikh/relative.html#data
[link 1]: http://vision.cs.utexas.edu/whittlesearch/
[Attribute Discovery Dataset]: http://tamaraberg.com/attributesDataset/index.html
[Caltech-UCSD Birds Dataset]: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
[Stanford Dogs Dataset]: http://vision.stanford.edu/aditya86/ImageNetDogs/
[Oxford-IIIT Pet Dataset]: http://www.robots.ox.ac.uk/~vgg/data/pets/
[Leeds Butterfly Dataset]: http://www.comp.leeds.ac.uk/scs6jwks/dataset/leedsbutterfly/
[Oxford Flower Dataset]: http://www.robots.ox.ac.uk/~vgg/data/flowers/
[www.cs.umass.edu_fddb]: http://www.cs.umass.edu/fddb/
[CMU_MIT]: http://vasc.ri.cmu.edu/idb/html/face/frontal_images/index.html
[Face Recognition Homepage]: http://www.face-rec.org/databases/
[NIST Face Homepage]: http://www.nist.gov/itl/iad/ig/face.cfm
[CMU Multi-PIE]: http://www.multipie.org/
[FERET]: http://www.nist.gov/itl/iad/ig/colorferet.cfm
[Deng Cai_s face dataset in Matlab Format]: http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html
[SCFace]: http://www.scface.org/
[INRIA Person Dataset]: http://pascal.inrialpes.fr/data/human/
[ETH Pedestrian Dataset]: http://www.vision.ee.ethz.ch/~aess/dataset/
[TUD-Brussels Pedestrian Dataset]: http://www.d2.mpi-inf.mpg.de/tud-brussels
[PASCAL Human Detection]: http://pascallin.ecs.soton.ac.uk/challenges/VOC/
[USC Pedestrian Dataset]: http://iris.usc.edu/Vision-Users/OldUsers/bowu/DatasetWebpage/dataset.html
[ImageNet]: http://www.image-net.org/
[Tiny Images]: http://groups.csail.mit.edu/vision/TinyImages/
[Caltech 101]: http://www.vision.caltech.edu/Image_Datasets/Caltech101/
[Caltech 256]: http://www.vision.caltech.edu/Image_Datasets/Caltech256/
[MIT LabelMe]: http://new-labelme.csail.mit.edu/Release3.0/index.php
[MIT SUN Dataset]: http://groups.csail.mit.edu/vision/SUN/
[UIUC Fifteen Scene Categories]: http://www-cvr.ai.uiuc.edu/ponce_grp/data/
[VGG Affine Dataset]: http://www.robots.ox.ac.uk/~vgg/data/data-aff.html
[VLBenchmarks]: http://www.vlfeat.org/benchmarks/index.html
[Benchmarking Activity Recognition]: http://rogerioferis.com/VisualRecognitionAndSearch/material/LiuFerisSunTutorial.pdf
[RGB-D Object Dataset]: http://www.cs.washington.edu/rgbd-dataset/index.html
[http_rogerioferis.com_VisualRecognitionAndSearch_Resources.html]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html
[http_www.vision.ee.ethz.ch_software_index.de.html]: http://www.vision.ee.ethz.ch/software/index.de.html%28%E5%BD%93%E7%84%B6%E8%BF%99%E5%8F%AA%E6%98%AF%E5%85%B6%E4%B8%AD%E4%B9%8B%E4%B8%80
[http_www.comp.hkbu.edu.hk_icpr06_tutorials_Pietikainen.html]: http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html
[FAST Corner Detection -- Edward Rosten]: http://mi.eng.cam.ac.uk/~er258/work/fast.html
[http_cbio.mskcc.org_aarvey_boosting_papers.html]: http://cbio.mskcc.org/~aarvey/boosting_papers.html
[http_graphics.cs.msu.ru_en_science_research_machinelearning_adaboosttoolbox]: http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox
[MultiBoost]: http://192.168.1.27/wiki/MultiBoost
[http_sourceforge.net_projects_multiboost]: http://sourceforge.net/projects/multiboost/
[TextonBoost]: http://192.168.1.27/wiki/TextonBoost
[Jamie Shotton - Code]: http://jamie.shotton.org/work/code.html
[LibSvm]: http://192.168.1.27/wiki/LibSvm
[http_www.csie.ntu.edu.tw_cjlin]: http://www.csie.ntu.edu.tw/~cjlin/
[Conditional Random Fields]: http://www.inference.phy.cam.ac.uk/hmw26/crf/
[CRF_ Yet Another CRF toolkit]: http://crfpp.sourceforge.net/
[Conditional Random Field _CRF_ Toolbox for Matlab]: http://www.computervisiononline.com/software/conditional-random-field-crf-toolbox-matlab
[Tree CRFs]: http://www.cs.cmu.edu/~jkbradle/TreeCRFs/
[LingPipe_ Installation]: http://alias-i.com/lingpipe/web/install.html
[Hidden Markov Models]: http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html
[Link 1]: http://blog.csdn.net/eaglex/article/details/6376826
[CvPapers]: http://192.168.1.27/wiki/CvPapers
[http_www.cvpapers.com]: http://www.cvpapers.com/
[http_peipa.essex.ac.uk_info_software.html]: http://peipa.essex.ac.uk/info/software.html
[https_skydrive.live.com_cid_3b6244088fd5a769_cid_3B6244088FD5A769_id_3B6244088FD5A769_523]: https://skydrive.live.com/?cid=3b6244088fd5a769#cid=3B6244088FD5A769&id=3B6244088FD5A769!523
[http_www.csse.uwa.edu.au_pk_research_matlabfns]: http://www.csse.uwa.edu.au/~pk/research/matlabfns/
[https_netfiles.uiuc.edu_jbhuang1_www_resources_vision_index.html]: https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html
[http_www.robots.ox.ac.uk_vgg_hzbook_code]: http://www.robots.ox.ac.uk/~vgg/hzbook/code/
[http_www.cvc.uab.cat_xbaro_eanbe_Software]: http://www.cvc.uab.cat/~xbaro/eanbe/#_Software
[Pablo Negri_s Home Page]: http://pablonegri.free.fr/index.html
[Jianxin Wu_s homepage]: http://c2inet.sce.ntu.edu.sg/Jianxin/index.html
[Peter Carbonetto]: http://www.cs.ubc.ca/~pcarbo/
[Detecting and Sketching the Common]: http://www.wisdom.weizmann.ac.il/~vision/SketchTheCommon/
[Pedro Felzenszwalb]: http://people.cs.uchicago.edu/~pff/
[Hae JONG_ SEO]: http://users.soe.ucsc.edu/~rokaf/interests.html
[CAP 5416 - Computer Vision]: http://www.cise.ufl.edu/class/cap5416fa09/Projects.html
[Parallel Tracking and Mapping for Small AR Workspaces _PTAM]: http://www.robots.ox.ac.uk/~gk/PTAM/
[Deva Ramanan - UC Irvine - Computer Vision]: http://www.ics.uci.edu/~dramanan/
[Raghuraman Gopalan]: http://www.umiacs.umd.edu/~raghuram/
[Hui Kong]: http://bmi.osu.edu/~hkong/index.htm
[Jamie Shotton - Post-Doctoral Researcher in Computer Vision]: http://jamie.shotton.org/work/index.html
[Jean-Yves AUDIBERT]: http://imagine.enpc.fr/~audibert/index.html
[Olga Veksler]: http://www.csd.uwo.ca/~olga/
[Stephen Gould]: http://users.cecs.anu.edu.au/~sgould/index.html#software
[Publications _Last Update_ 09_30_10]: http://faculty.ucmerced.edu/mhyang/code.html
[Karim Ali - FlowBoost]: http://cvlab.epfl.ch/~ali/flowboost.htm
[A simple parts and structure object detector]: http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html
[Code - Oxford Brookes Vision Group]: http://cms.brookes.ac.uk/research/visiongroup/code.php
[Taku Kudo]: http://chasen.org/~taku/index.html.en
[INRIA Pedestrian detector]: http://www.cs.berkeley.edu/~smaji/projects/ped-detector/
[William Robson Schwartz - Softwares]: http://www.liv.ic.unicamp.br/~wschwartz/softwares.html
[calvin upper-body detector v1.02]: http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/
[RPT_CVG]: http://www.cvg.rdg.ac.uk/software/rpt/index.html
[Main Page]: http://www.idiap.ch/~odobez/human-detection/index.html
[Source Code]: http://www.lienhart.de/Source_Code/source_code.html
[Dr. Luciano Spinello]: http://www.informatik.uni-freiburg.de/~spinello/people2D.html
[Pedestrian Detection]: http://bmi.osu.edu/~hkong/Human_Detection.html
[Class-Specific Hough Forests for Object Detection]: http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html
[High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning]: http://www.cs.cornell.edu/~asaxena/rccar/
[TLD]: http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html
[online boosting trackers]: http://www.vision.ee.ethz.ch/boostingTrackers/
[Object Detection]: http://www.irisa.fr/vista/Equipe/People/Laptev/objectdetection.html
[Software for object detection]: http://www.seas.upenn.edu/~limingw/obj_det_accv07/code.html
[10]: http://itp.nyu.edu/~mbe230/blogmer/2011/02/10-face-detection-projects/
[An ICA page-papers_code_demo_links _Tony Bell]: http://cnl.salk.edu/~tony/ica.html
[FastICA]: http://research.ics.tkk.fi/ica/fastica/
[Cached k-d tree search for ICP algorithms]: http://kos.informatik.uni-osnabrueck.de/download/3dim2007/paper.html
[The Kalman Filter]: http://www.cs.unc.edu/~welch/kalman/index.html
[The Bayesian Filtering Library]: http://www.orocos.org/bfl
[Source Code 1]: http://www.multimedia-computing.de/wiki/Source_Code#Dataset_of_logos_in_real-world_images:_FlickrLogos-32
[Vanishing point detection for general road detection]: http://bmi.osu.edu/~hkong/Road_Detection.html
[SLIC Superpixels]: http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html