计算机视觉、机器学习相关领域论文和源代码

计算机视觉、机器学习相关领域论文和源代码

From：http://user.qzone.qq.com/114772751/blog/1408416377

一、特征提取Feature Extraction：

[L]·         SIFT [1] [Demo program][SIFT Library] [VLFeat][/L]
[L]·         PCA-SIFT [2] [Project][/L]
[L]·         Affine-SIFT [3] [Project][/L]
[L]·         SURF [4] [OpenSURF] [Matlab Wrapper][/L]
[L]·         Affine Covariant Features [5] [Oxford project][/L]
[L]·         MSER [6] [Oxford project] [VLFeat][/L]
[L]·         Geometric Blur [7] [Code][/L]
[L]·         Local Self-Similarity Descriptor [8] [Oxford implementation][/L]
[L]·         Global and Efficient Self-Similarity [9] [Code][/L]
[L]·         Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows][/L]
[L]·         GIST [11] [Project][/L]
[L]·         Shape Context [12] [Project][/L]
[L]·         Color Descriptor [13] [Project][/L]
[L]·         Pyramids of Histograms of Oriented Gradients [Code][/L]
[L]·         Space-Time Interest Points (STIP) [14][Project] [Code][/L]
[L]·         Boundary Preserving Dense Local Regions [15][Project][/L]
[L]·         Weighted Histogram[Code][/L]
[L]·         Histogram-based Interest Points Detectors[Paper][Code][/L]
[L]·         An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project][/L]
[L]·         Fast Sparse Representation with Prototypes[Project][/L]
[L]·         Corner Detection [Project][/L]
[L]·         AGAST Corner Detector: faster than FAST and even FAST-ER[Project][/L]
[L]·         Real-time Facial Feature Detection using Conditional Regression Forests[Project]
[/L]
[L]·         Global and Efficient Self-Similarity for Object Classification and Detection[code]
[/L]
[L]·         WαSH: Weighted α-Shapes for Local Feature Detection[Project]
[/L]
[L]·         HOG[Project][/L]
[L]·         Online Selection of Discriminative Tracking Features[Project]
[/L]
[L]                        [/L]
[L]二、图像分割Image Segmentation：[/L]
[L]·           Normalized Cut [1] [Matlab code][/L]
[L]·           Gerg Mori’ Superpixel code [2] [Matlab code][/L]
[L]·           Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper][/L]
[L]·           Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper][/L]
[L]·           OWT-UCM Hierarchical Segmentation [5] [Resources][/L]
[L]·           Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code][/L]
[L]·           Quick-Shift [7] [VLFeat][/L]
[L]·           SLIC Superpixels [8] [Project][/L]
[L]·           Segmentation by Minimum Code Length [9] [Project][/L]
[L]·           Biased Normalized Cut [10] [Project][/L]
[L]·           Segmentation Tree [11-12] [Project][/L]
[L]·           Entropy Rate Superpixel Segmentation [13] [Code][/L]
[L]·           Fast Approximate Energy Minimization via Graph Cuts[Paper][Code][/L]
[L]·           Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code][/L]
[L]·           Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code][/L]
[L]·           Random Walks for Image Segmentation[Paper][Code][/L]
[L]·           Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code][/L]
[L]·           An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code][/L]
[L]·           Geodesic Star Convexity for Interactive Image Segmentation[Project][/L]
[L]·           Contour Detection and Image Segmentation Resources[Project][Code][/L]
[L]·           Biased Normalized Cuts[Project][/L]
[L]·           Max-flow/min-cut[Project][/L]
[L]·           Chan-Vese Segmentation using Level Set[Project][/L]
[L]·           A Toolbox of Level Set Methods[Project][/L]
[L]·           Re-initialization Free Level Set Evolution via Reaction Diffusion[Project][/L]
[L]·           Improved C-V active contour model[Paper][Code][/L]
[L]·           A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code][/L]
[L]·          Level Set Method Research by Chunming Li[Project]
[/L]
[L]·          ClassCut for Unsupervised Class Segmentation[code]
[/L]
[L]·         SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]
[/L]
[L] [/L]
[L]三、目标检测Object Detection：[/L]
[L]·           A simple object detector with boosting [Project][/L]
[L]·           INRIA Object Detection and Localization Toolkit [1] [Project][/L]
[L]·           Discriminatively Trained Deformable Part Models [2] [Project][/L]
[L]·           Cascade Object Detection with Deformable Part Models [3] [Project][/L]
[L]·           Poselet [4] [Project][/L]
[L]·           Implicit Shape Model [5] [Project][/L]
[L]·           Viola and Jones’s Face Detection [6] [Project][/L]
[L]·           Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code][/L]
[L]·           Hand detection using multiple proposals[Project][/L]
[L]·           Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code][/L]
[L]·           Discriminatively trained deformable part models[Project][/L]
[L]·           Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project][/L]
[L]·           Image Processing On Line[Project][/L]
[L]·           Robust Optical Flow Estimation[Project][/L]
[L]·           Where's Waldo: Matching People in Images of Crowds[Project][/L]
[L]·           Scalable Multi-class Object Detection[Project]
[/L]
[L]·           Class-Specific Hough Forests for Object Detection[Project]
[/L]
[L]·         Deformed Lattice Detection In Real-World Images[Project]
[/L]
[L]·         Discriminatively trained deformable part models[Project]
[/L]
[L] [/L]
[L]四、显著性检测Saliency Detection：[/L]
[L]·           Itti, Koch, and Niebur’ saliency detection [1] [Matlab code][/L]
[L]·           Frequency-tuned salient region detection [2] [Project][/L]
[L]·           Saliency detection using maximum symmetric surround [3] [Project][/L]
[L]·           Attention via Information Maximization [4] [Matlab code][/L]
[L]·           Context-aware saliency detection [5] [Matlab code][/L]
[L]·           Graph-based visual saliency [6] [Matlab code][/L]
[L]·           Saliency detection: A spectral residual approach. [7] [Matlab code][/L]
[L]·           Segmenting salient objects from images and videos. [8] [Matlab code][/L]
[L]·           Saliency Using Natural statistics. [9] [Matlab code][/L]
[L]·           Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code][/L]
[L]·           Learning to Predict Where Humans Look [11] [Project][/L]
[L]·           Global Contrast based Salient Region Detection [12] [Project][/L]
[L]·           Bayesian Saliency via Low and Mid Level Cues[Project][/L]
[L]·           Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code][/L]
[L]·         Saliency Detection: A Spectral Residual Approach[Code]
[/L]
[L] [/L]
[L]五、图像分类、聚类Image Classification, Clustering[/L]
[L]·           Pyramid Match [1] [Project][/L]
[L]·           Spatial Pyramid Matching [2] [Code][/L]
[L]·           Locality-constrained Linear Coding [3] [Project] [Matlab code][/L]
[L]·           Sparse Coding [4] [Project] [Matlab code][/L]
[L]·           Texture Classification [5] [Project][/L]
[L]·           Multiple Kernels for Image Classification [6] [Project][/L]
[L]·           Feature Combination [7] [Project][/L]
[L]·           SuperParsing [Code][/L]
[L]·           Large Scale Correlation Clustering Optimization[Matlab code][/L]
[L]·           Detecting and Sketching the Common[Project][/L]
[L]·           Self-Tuning Spectral Clustering[Project][Code][/L]
[L]·           User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code][/L]
[L]·           Filters for Texture Classification[Project][/L]
[L]·           Multiple Kernel Learning for Image Classification[Project][/L]
[L]·          SLIC Superpixels[Project]
[/L]
[L] [/L]
[L]六、抠图Image Matting[/L]
[L]·           A Closed Form Solution to Natural Image Matting [Code][/L]
[L]·           Spectral Matting [Project][/L]
[L]·           Learning-based Matting [Code][/L]
[L] [/L]
[L]七、目标跟踪Object Tracking：[/L]
[L]·           A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project][/L]
[L]·           Object Tracking via Partial Least Squares Analysis[Paper][Code][/L]
[L]·           Robust Object Tracking with Online Multiple Instance Learning[Paper][Code][/L]
[L]·           Online Visual Tracking with Histograms and Articulating Blocks[Project][/L]
[L]·           Incremental Learning for Robust Visual Tracking[Project][/L]
[L]·           Real-time Compressive Tracking[Project][/L]
[L]·           Robust Object Tracking via Sparsity-based Collaborative Model[Project][/L]
[L]·           Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project][/L]
[L]·           Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code][/L]
[L]·           Superpixel Tracking[Project][/L]
[L]·           Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code][/L]
[L]·           Online Multiple Support Instance Tracking [Paper][Code][/L]
[L]·           Visual Tracking with Online Multiple Instance Learning[Project][/L]
[L]·           Object detection and recognition[Project][/L]
[L]·           Compressive Sensing Resources[Project][/L]
[L]·           Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project][/L]
[L]·           Tracking-Learning-Detection[Project][OpenTLD/C++ Code][/L]
[L]·           the HandVu：vision-based hand gesture interface[Project]
[/L]
[L]·           Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]
[/L]
[L] [/L]
[L]八、Kinect：[/L]
[L]·           Kinect toolbox[Project][/L]
[L]·           OpenNI[Project][/L]
[L]·           zouxy09 优快云 Blog[Resource][/L]
[L]·           FingerTracker 手指跟踪[code]
[/L]
[L] [/L]
[L]九、3D相关：[/L]
[L]·           3D Reconstruction of a Moving Object[Paper] [Code][/L]
[L]·           Shape From Shading Using Linear Approximation[Code][/L]
[L]·           Combining Shape from Shading and Stereo Depth Maps[Project][Code][/L]
[L]·           Shape from Shading: A Survey[Paper][Code][/L]
[L]·           A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code][/L]
[L]·           Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code][/L]
[L]·           A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code][/L]
[L]·           Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project][/L]
[L]·           Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code][/L]
[L]·           Learning 3-D Scene Structure from a Single Still Image[Project][/L]
[L] [/L]
[L]十、机器学习算法：[/L]
[L]·           Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library][/L]
[L]·           Random Sampling[code][/L]
[L]·           Probabilistic Latent Semantic Analysis (pLSA)[Code][/L]
[L]·           FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project][/L]
[L]·           Fast Intersection / Additive Kernel SVMs[Project][/L]
[L]·           SVM[Code][/L]
[L]·           Ensemble learning[Project][/L]
[L]·           Deep Learning[Net][/L]
[L]·           Deep Learning Methods for Vision[Project]
[/L]
[L]·           Neural Network for Recognition of Handwritten Digits[Project][/L]
[L]·           Training a deep autoencoder or a classifier on MNIST digits[Project][/L]
[L]·          THE MNIST DATABASE of handwritten digits[Project]
[/L]
[L]·          Ersatz：deep neural networks in the cloud[Project]
[/L]
[L]·          Deep Learning [Project]
[/L]
[L]·          sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
[/L]
[L]·          Weka 3: Data Mining Software in Java[Project]
[/L]
[L]·          Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]
[/L]
[L]·          CNN - Convolutional neural network class[Matlab Tool]
[/L]
[L]·          Yann LeCun's Publications[Wedsite]
[/L]
[L]·          LeNet-5, convolutional neural networks[Project]
[/L]
[L]·          Training a deep autoencoder or a classifier on MNIST digits[Project]
[/L]
[L]·          Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]
[/L]
[L]·         Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]
[/L]
[L]·         Sparse coding simulation software[Project]
[/L]
[L]·         Visual Recognition and Machine Learning Summer School[Software]
[/L]
[L] [/L]
[L]十一、目标、行为识别Object, Action Recognition：[/L]
[L]·           Action Recognition by Dense Trajectories[Project][Code][/L]
[L]·           Action Recognition Using a Distributed Representation of Pose and Appearance[Project][/L]
[L]·           Recognition Using Regions[Paper][Code][/L]
[L]·           2D Articulated Human Pose Estimation[Project][/L]
[L]·           Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code][/L]
[L]·           Estimating Human Pose from Occluded Images[Paper][Code][/L]
[L]·           Quasi-dense wide baseline matching[Project][/L]
[L]·           ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]
[/L]
[L]·           Real Time Head Pose Estimation with Random Regression Forests[Project]
[/L]
[L]·           2D Action Recognition Serves 3D Human Pose Estimation[Project]
[/L]
[L]·           A Hough Transform-Based Voting Framework for Action Recognition[Project]
[/L]
[L]·           Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]
[/L]
[L]·         2D articulated human pose estimation software[Project]
[/L]
[L]·         Learning and detecting shape models [code]
[/L]
[L]·         Progressive Search Space Reduction for Human Pose Estimation[Project]
[/L]
[L]·         Learning Non-Rigid 3D Shape from 2D Motion[Project]
[/L]
[L] [/L]
[L]十二、图像处理：[/L]
[L]·         Distance Transforms of Sampled Functions[Project][/L]
[L]·         The Computer Vision Homepage[Project]
[/L]
[L]·         Efficient appearance distances between windows[code]
[/L]
[L]·         Image Exploration algorithm[code]
[/L]
[L]·         Motion Magnification 运动放大 [Project]
[/L]
[L]·         Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]
[/L]
[L]·         A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]
[/L]
[L]                  [/L]
[L]十三、一些实用工具：[/L]
[L]·           EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code][/L]
[L]·           a development kit of matlab mex functions for OpenCV library[Project][/L]
[L]·           Fast Artificial Neural Network Library[Project][/L]
[L] [/L]

[L]十四、人手及指尖检测与识别：[/L]
[L]·           finger-detection-and-gesture-recognition [Code][/L]
[L]·           Hand and Finger Detection using JavaCV[Project][/L]
[L]·           Hand and fingers detection[Code][/L]
[L]
[/L]
[L][/L]
[L]十五、场景解释：[/L]
[L]·           Nonparametric Scene Parsing via Label Transfer [Project][/L]
[L]
[/L]
[L][/L]
[L]十六、光流Optical flow：[/L]
[L]·         High accuracy optical flow using a theory for warping [Project][/L]
[L]·         Dense Trajectories Video Description [Project]
[/L]
[L]·         SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]
[/L]
[L]·         KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]
[/L]
[L]·         Tracking Cars Using Optical Flow[Project]
[/L]
[L]·         Secrets of optical flow estimation and their principles[Project]
[/L]
[L]·         implmentation of the Black and Anandan dense optical flow method[Project]
[/L]
[L]·         Optical Flow Computation[Project]
[/L]
[L]·         Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]
[/L]
[L]·         A Database and Evaluation Methodology for Optical Flow[Project]
[/L]
[L]·         optical flow relative[Project][/L]
[L]·         Robust Optical Flow Estimation [Project]
[/L]
[L]·         optical flow[Project]
[/L]


[L]十七、图像检索Image Retrieval：[/B][/L]
[L]·           Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code][/L]
[L]
[/L]
[L][/L]
[L]十八、马尔科夫随机场Markov Random Fields：[/L]
[L]·         Markov Random Fields for Super-Resolution [Project][/L]
[L]·         A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]
[/L]


[L]十九、运动检测Motion detection：[/L]
[L]·         Moving Object Extraction, Using Models or Analysis of Regions [Project][/L]
[L]·         Background Subtraction: Experiments and Improvements for ViBe [Project][/L]
[L]·         A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]
[/L]
[L]·         changedetection.net: A new change detection benchmark dataset[Project]
[/L]
[L]·         ViBe - a powerful technique for background detection and subtraction in video sequences[Project]
[/L]
[L]·         Background Subtraction Program[Project]
[/L]
[L]·         Motion Detection Algorithms[Project]
[/L]
[L]·         Stuttgart Artificial Background Subtraction Dataset[Project]
[/L]
[L]·         Object Detection, Motion Estimation, and Tracking[Project][/L]