一、特徵提取Feature Extraction：

• PCA-SIFT [2] [Project]
• Affine-SIFT [3] [Project]
• Affine Covariant Features [5] [Oxford project]
• Geometric Blur [7] [Code]
• Local Self-Similarity Descriptor [8] [Oxford implementation]
• Global and Efficient Self-Similarity [9] [Code]
• 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]
• Online Selection of Discriminative Tracking Features[Project]

二、影象分割Image Segmentation：

• Normalized Cut [1] [Matlab code]
• Gerg Mori’ Superpixel code [2] [Matlab code]
• Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
• OWT-UCM Hierarchical Segmentation [5] [Resources]
• 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、目標檢測Object Detection：

• A simple object detector with boosting [Project]
• INRIA Object Detection and Localization Toolkit [1] [Project]
• Discriminatively Trained Deformable Part Models [2] [Project]
• Cascade Object Detection with Deformable Part Models [3] [Project]
• Poselet [4] [Project]
• Implicit Shape Model [5] [Project]
• Viola and Jones’s Face Detection [6] [Project]
• Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]
• Hand detection using multiple proposals[Project]
• Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]
• Discriminatively trained deformable part models[Project]
• Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]
• Image Processing On Line[Project]
• Robust Optical Flow Estimation[Project]
• Where’s Waldo: Matching People in Images of Crowds[Project]
• Scalable Multi-class Object Detection[Project]
• Class-Specific Hough Forests for Object Detection[Project]
• Deformed Lattice Detection In Real-World Images[Project]
• Discriminatively trained deformable part models[Project]

四、顯著性檢測Saliency Detection：

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

五、影象分類、聚類Image Classification, Clustering

• Pyramid Match [1] [Project]
• Spatial Pyramid Matching [2] [Code]
• Locality-constrained Linear Coding [3] [Project] [Matlab code]
• Texture Classification [5] [Project]
• Multiple Kernels for Image Classification [6] [Project]
• Feature Combination [7] [Project]
• SuperParsing [Code]
• Large Scale Correlation Clustering Optimization[Matlab code]
• Detecting and Sketching the Common[Project]
• Self-Tuning Spectral Clustering[Project][Code]
• User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]
• Filters for Texture Classification[Project]
• Multiple Kernel Learning for Image Classification[Project]
• SLIC Superpixels[Project]

六、摳圖Image Matting

• A Closed Form Solution to Natural Image Matting [Code]
• Spectral Matting [Project]
• Learning-based Matting [Code]

七、目標跟蹤Object Tracking：

• A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]
• Object Tracking via Partial Least Squares Analysis[Paper][Code]
• Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]
• Online Visual Tracking with Histograms and Articulating Blocks[Project]
• Incremental Learning for Robust Visual Tracking[Project]
• Real-time Compressive Tracking[Project]
• Robust Object Tracking via Sparsity-based Collaborative Model[Project]
• Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]
• Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]
• Superpixel Tracking[Project]
• Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]
• Online Multiple Support Instance Tracking [Paper][Code]
• Visual Tracking with Online Multiple Instance Learning[Project]
• Object detection and recognition[Project]
• Compressive Sensing Resources[Project]
• Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]
• 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]
• zouxy09 CSDN Blog[Resource]
• FingerTracker 手指跟蹤[code]

九、3D相關：

• 3D Reconstruction of a Moving Object[Paper] [Code]
• Shape From Shading Using Linear Approximation[Code]
• Combining Shape from Shading and Stereo Depth Maps[Project][Code]
• Shape from Shading: A Survey[Paper][Code]
• A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
• Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
• A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
• Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
• Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
• Learning 3-D Scene Structure from a Single Still Image[Project]

十、機器學習演算法：

• Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]
• Random Sampling[code]
• Probabilistic Latent Semantic Analysis (pLSA)[Code]
• FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
• Fast Intersection / Additive Kernel SVMs[Project]
• SVM[Code]
• Ensemble learning[Project]
• Deep Learning[Net]
• Deep Learning Methods for Vision[Project]
• Neural Network for Recognition of Handwritten Digits[Project]
• Training a deep autoencoder or a classifier on MNIST digits[Project]
• THE MNIST DATABASE of handwritten digits[Project]
• Ersatz：deep neural networks in the cloud[Project]
• Deep Learning [Project]
• sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
• Weka 3: Data Mining Software in Java[Project]
• Invited talk “A Tutorial on Deep Learning” by Dr. Kai Yu (餘凱)[Video]
• CNN - Convolutional neural network class[Matlab Tool]
• Yann LeCun’s Publications[Wedsite]
• LeNet-5, convolutional neural networks[Project]
• Training a deep autoencoder or a classifier on MNIST digits[Project]
• Deep Learning 大牛Geoffrey E. Hinton’s HomePage[Website]
• Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]
• Sparse coding simulation software[Project]
• Visual Recognition and Machine Learning Summer School[Software]

十一、目標、行為識別Object, Action Recognition：

• Action Recognition by Dense Trajectories[Project][Code]
• Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
• Recognition Using Regions[Paper][Code]
• 2D Articulated Human Pose Estimation[Project]
• Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
• Estimating Human Pose from Occluded Images[Paper][Code]
• Quasi-dense wide baseline matching[Project]
• ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]
• Real Time Head Pose Estimation with Random Regression Forests[Project]
• 2D Action Recognition Serves 3D Human Pose Estimation[Project]
• A Hough Transform-Based Voting Framework for Action Recognition[Project]
• Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]
• 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、影象處理：

• Distance Transforms of Sampled Functions[Project]
• The Computer Vision Homepage[Project]
• Efficient appearance distances between windows[code]
• Image Exploration algorithm[code]
• Motion Magnification 運動放大 [Project]
• Bilateral Filtering for Gray and Color Images 雙邊濾波器 [Project]
• A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

十三、一些實用工具：

• EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
• a development kit of matlab mex functions for OpenCV library[Project]
• Fast Artificial Neural Network Library[Project]

十四、人手及指尖檢測與識別：

• finger-detection-and-gesture-recognition [Code]
• Hand and Finger Detection using JavaCV[Project]
• Hand and fingers detection[Code]

十五、場景解釋：

• Nonparametric Scene Parsing via Label Transfer [Project]

十六、光流Optical flow：

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

十七、影象檢索Image Retrieval：

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

十八、馬爾科夫隨機場Markov Random Fields：

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

十九、運動檢測Motion detection：

• Moving Object Extraction, Using Models or Analysis of Regions [Project]
• Background Subtraction: Experiments and Improvements for ViBe [Project]
• A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]
• changedetection.net: A new change detection benchmark dataset[Project]
• ViBe - a powerful technique for background detection and subtraction in video sequences[Project]
• Background Subtraction Program[Project]
• Motion Detection Algorithms[Project]
• Stuttgart Artificial Background Subtraction Dataset[Project]
• Object Detection, Motion Estimation, and Tracking[Project]

Feature Detection and Description

General Libraries: 

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

Fast Keypoint Detectors for Real-time Applications: 

• FAST – High-speed corner detector implementation for a wide variety of platforms
• AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV 2010).

Binary Descriptors for Real-Time Applications: 

• BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)
• ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)
• BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)
• FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)

SIFT and SURF Implementations: 

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,