原文地址：作者：

注：下面有project网站的大部分都有paper和相应的code。Code一般是C/C++或者Matlab代码。

最近一次更新：2013-1-29

一、 特征提取Feature Extraction：

SIFT [1] [][] []

PCA-SIFT [2] []

Affine-SIFT [3] []

SURF [4] [] []

Affine Covariant Features [5] []

MSER [6] [] []

Geometric Blur [7] []

Local Self-Similarity Descriptor [8] []

Global and Efficient Self-Similarity [9] []

Histogram of Oriented Graidents [10] [] []

GIST [11] []

Shape Context [12] []

Color Descriptor [13] []

Pyramids of Histograms of Oriented Gradients []

Space-Time Interest Points (STIP) [14][] []

Boundary Preserving Dense Local Regions [15][]

Weighted Histogram[]

Histogram-based Interest Points Detectors[][]

An OpenCV - C++ implementation of Local Self Similarity Descriptors []

Fast Sparse Representation with Prototypes[]

Corner Detection []

AGAST Corner Detector: faster than FAST and even FAST-ER[]

二、 图像分割Image Segmentation：

Normalized Cut [1] []

Gerg Mori’ Superpixel code [2] []

Efficient Graph-based Image Segmentation [3] [] []

Mean-Shift Image Segmentation [4] [] []

OWT-UCM Hierarchical Segmentation [5] []

Turbepixels [6] [] [] []

Quick-Shift [7] []

SLIC Superpixels [8] []

Segmentation by Minimum Code Length [9] []

Biased Normalized Cut [10] []

Segmentation Tree [11-12] []

Entropy Rate Superpixel Segmentation [13] []

Fast Approximate Energy Minimization via Graph Cuts[][]

Efficient Planar Graph Cuts with Applications in Computer Vision[][]

Isoperimetric Graph Partitioning for Image Segmentation[][]

Random Walks for Image Segmentation[][]

Blossom V: A new implementation of a minimum cost perfect matching algorithm[]

An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[][]

Geodesic Star Convexity for Interactive Image Segmentation[]

Contour Detection and Image Segmentation Resources[][]

Biased Normalized Cuts[]

Max-flow/min-cut[]

Chan-Vese Segmentation using Level Set[]

A Toolbox of Level Set Methods[]

Re-initialization Free Level Set Evolution via Reaction Diffusion[]

Improved C-V active contour model[][]

A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[][]

Level Set Method Research by Chunming Li[]

三、 目标检测Object Detection：

A simple object detector with boosting []

INRIA Object Detection and Localization Toolkit [1] []

Discriminatively Trained Deformable Part Models [2] []

Cascade Object Detection with Deformable Part Models [3] []

Poselet [4] []

Implicit Shape Model [5] []

Viola and Jones’s Face Detection [6] []

Bayesian Modelling of Dyanmic Scenes for Object Detection[][]

Hand detection using multiple proposals[]

Color Constancy, Intrinsic Images, and Shape Estimation[][]

Discriminatively trained deformable part models[]

Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD []

Image Processing On Line[]

Robust Optical Flow Estimation[]

Where's Waldo: Matching People in Images of Crowds[]

四、 显著性检测Saliency Detection：

Itti, Koch, and Niebur’ saliency detection [1] []

Frequency-tuned salient region detection [2] []

Saliency detection using maximum symmetric surround [3] []

Attention via Information Maximization [4] []

Context-aware saliency detection [5] []

Graph-based visual saliency [6] []

Saliency detection: A spectral residual approach. [7] []

Segmenting salient objects from images and videos. [8] []

Saliency Using Natural statistics. [9] []

Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] []

Learning to Predict Where Humans Look [11] []

Global Contrast based Salient Region Detection [12] []

Bayesian Saliency via Low and Mid Level Cues[]

Top-Down Visual Saliency via Joint CRF and Dictionary Learning[][]

五、 图像分类、聚类Image Classification, Clustering

Pyramid Match [1] []

Spatial Pyramid Matching [2] []

Locality-constrained Linear Coding [3] [] []

Sparse Coding [4] [] []

Texture Classification [5] []

Multiple Kernels for Image Classification [6] []

Feature Combination [7] []

SuperParsing []

Large Scale Correlation Clustering Optimization[]

Detecting and Sketching the Common[]

Self-Tuning Spectral Clustering[][]

User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[][]

Filters for Texture Classification[]

Multiple Kernel Learning for Image Classification[]

SLIC Superpixels[]

六、 抠图Image Matting

A Closed Form Solution to Natural Image Matting []

Spectral Matting []

Learning-based Matting []

七、 目标跟踪Object Tracking：

A Forest of Sensors - Tracking Adaptive Background Mixture Models []

Object Tracking via Partial Least Squares Analysis[][]

Robust Object Tracking with Online Multiple Instance Learning[][]

Online Visual Tracking with Histograms and Articulating Blocks[]

Incremental Learning for Robust Visual Tracking[]

Real-time Compressive Tracking[]

Robust Object Tracking via Sparsity-based Collaborative Model[]

Visual Tracking via Adaptive Structural Local Sparse Appearance Model[]

Online Discriminative Object Tracking with Local Sparse Representation[][]

Superpixel Tracking[]

Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[][]

Online Multiple Support Instance Tracking [][]

Visual Tracking with Online Multiple Instance Learning[]

Object detection and recognition[]

Compressive Sensing Resources[]

Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[]

Tracking-Learning-Detection[][]

the HandVu：vision-based hand gesture interface[]

八、 Kinect：

Kinect toolbox[]

OpenNI[]

zouxy09 CSDN Blog[]

九、 3D相关：

3D Reconstruction of a Moving Object[] []

Shape From Shading Using Linear Approximation[]

Combining Shape from Shading and Stereo Depth Maps[][]

Shape from Shading: A Survey[][]

A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[][]

Multi-camera Scene Reconstruction via Graph Cuts[][]

A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[][]

Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[]

Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[]

Learning 3-D Scene Structure from a Single Still Image[]

十、 机器学习算法：

Matlab class for computing Approximate Nearest Nieghbor (ANN) [ providing interface to]

Random Sampling[]

Probabilistic Latent Semantic Analysis (pLSA)[]

FASTANN and FASTCLUSTER for approximate k-means (AKM)[]

Fast Intersection / Additive Kernel SVMs[]

SVM[]

Ensemble learning[]

Deep Learning[]

Deep Learning Methods for Vision[]

Neural Network for Recognition of Handwritten Digits[]

Training a deep autoencoder or a classifier on MNIST digits[]

THE MNIST DATABASE of handwritten digits[]

Ersatz：deep neural networks in the cloud[]

Deep Learning []

sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[]

Weka 3: Data Mining Software in Java[]

Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[]

CNN - Convolutional neural network class[]

Yann LeCun's Publications[]

LeNet-5, convolutional neural networks[]

Training a deep autoencoder or a classifier on MNIST digits[]

Deep Learning 大牛Geoffrey E. Hinton's HomePage[]

十一、 目标、行为识别Object, Action Recognition：

Action Recognition by Dense Trajectories[][]

Action Recognition Using a Distributed Representation of Pose and Appearance[]

Recognition Using Regions[][]

2D Articulated Human Pose Estimation[]

Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[][]

Estimating Human Pose from Occluded Images[][]

Quasi-dense wide baseline matching[]

ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[]

十二、 图像处理：

Distance Transforms of Sampled Functions[]

The Computer Vision Homepage[]

十三、 一些实用工具：

EGT: a Toolbox for Multiple View Geometry and Visual Servoing[] []

a development kit of matlab mex functions for OpenCV library[]

Fast Artificial Neural Network Library[]

From:http://blog.csdn.net/zouxy09