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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[]
• Real-time Facial Feature Detection using Conditional Regression Forests[]
• Global and Efficient Self-Similarity for Object Classification and Detection[]
• WαSH: Weighted α-Shapes for Local Feature Detection[]
• HOG[]
• Online Selection of Discriminative Tracking Features[]

 

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[]
• ClassCut for Unsupervised Class Segmentation[e]
• SEEDS: Superpixels Extracted via Energy-Driven Sampling ][]

 

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[]
• Scalable Multi-class Object Detection[]
• Class-Specific Hough Forests for Object Detection[]
• Deformed Lattice Detection In Real-World Images[]
• Discriminatively trained deformable part models[]

 

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[][]
• Saliency Detection: A Spectral Residual Approach[]

 

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

 

八、Kinect：

• Kinect toolbox[]
• OpenNI[]
• zouxy09 CSDN Blog[]
• FingerTracker 手指跟踪[]

 

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[]
• Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[]
• Sparse coding simulation software[]
• Visual Recognition and Machine Learning Summer School[]

 

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[]
• Real Time Head Pose Estimation with Random Regression Forests[]
• 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[]
• Learning and detecting shape models []
• Progressive Search Space Reduction for Human Pose Estimation[]
• Learning Non-Rigid 3D Shape from 2D Motion[]

 

十二、图像处理：

• Distance Transforms of Sampled Functions[]
• The Computer Vision Homepage[]
• Efficient appearance distances between windows[]
• Image Exploration algorithm[]
• Motion Magnification 运动放大 []
• Bilateral Filtering for Gray and Color Images 双边滤波器 []
• 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[] []
• a development kit of matlab mex functions for OpenCV library[]
• Fast Artificial Neural Network Library[]

 

十四、人手及指尖检测与识别：

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

 

十五、场景解释：

• Nonparametric Scene Parsing via Label Transfer []

 

十六、光流Optical flow：

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

 

十七、图像检索Image Retrieval：

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

 

十八、马尔科夫随机场Markov Random Fields：

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

 

十九、运动检测Motion detection：

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