[转]机器视觉开源代码集合

一、特征提取Feature Extraction：

l  SIFT [1] [Demo program][SIFT Library] [VLFeat]

l  PCA-SIFT [2] [Project]

l  Affine-SIFT [3] [Project]

l  SURF [4] [OpenSURF] [Matlab Wrapper]

l  Affine Covariant Features [5] [Oxford project]

l  MSER [6] [Oxford project] [VLFeat]

l  Geometric Blur [7] [Code]

l  Local Self-Similarity Descriptor [8] [Oxford implementation]

l  Global and Efficient Self-Similarity [9] [Code]

l  Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]

l  GIST [11] [Project]

l  Shape Context [12] [Project]

l  Color Descriptor [13] [Project]

l  Pyramids of Histograms of Oriented Gradients [Code]

l  Space-Time Interest Points (STIP) [14][Project] [Code]

l  Boundary Preserving Dense Local Regions [15][Project]

l  Weighted Histogram[Code]

l  Histogram-based Interest Points Detectors[Paper][Code]

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

l  Fast Sparse Representation with Prototypes[Project]

l  Corner Detection [Project]

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

l  Real-time Facial Feature Detection using Conditional Regression Forests[Project]

l  Global and Efficient Self-Similarity for Object Classification and Detection[code]

l  WαSH: Weighted α-Shapes for Local Feature Detection[Project]

l  HOG[Project]

l  Online Selection of Discriminative Tracking Features[Project]

 

二、图像分割Image Segmentation：

l  Normalized Cut [1] [Matlab code]

l  Gerg Mori’ Superpixel code [2] [Matlab code]

l  Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]

l  Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]

l  OWT-UCM Hierarchical Segmentation [5] [Resources]

l  Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]

l  Quick-Shift [7] [VLFeat]

l  SLIC Superpixels [8] [Project]

l  Segmentation by Minimum Code Length [9] [Project]

l  Biased Normalized Cut [10] [Project]

l  Segmentation Tree [11-12] [Project]

l  Entropy Rate Superpixel Segmentation [13] [Code]

l  Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]

l  Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]

l  Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]

l  Random Walks for Image Segmentation[Paper][Code]

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

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

l  Geodesic Star Convexity for Interactive Image Segmentation[Project]

l  Contour Detection and Image Segmentation Resources[Project][Code]

l  Biased Normalized Cuts[Project]

l  Max-flow/min-cut[Project]

l  Chan-Vese Segmentation using Level Set[Project]

l  A Toolbox of Level Set Methods[Project]

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

l  Improved C-V active contour model[Paper][Code]

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

l  Level Set Method Research by Chunming Li[Project]

l  ClassCut for Unsupervised Class Segmentation[code]

l  SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]

 

三、目标检测Object Detection：

l  A simple object detector with boosting [Project]

l  INRIA Object Detection and Localization Toolkit [1] [Project]

l  Discriminatively Trained Deformable Part Models [2] [Project]

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

l  Poselet [4] [Project]

l  Implicit Shape Model [5] [Project]

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

l  Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]

l  Hand detection using multiple proposals[Project]

l  Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]

l  Discriminatively trained deformable part models[Project]

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

l  Image Processing On Line[Project]

l  Robust Optical Flow Estimation[Project]

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

l  Scalable Multi-class Object Detection[Project]

l  Class-Specific Hough Forests for Object Detection[Project]

l  Deformed Lattice Detection In Real-World Images[Project]

l  Discriminatively trained deformable part models[Project]

 

四、显著性检测Saliency Detection：

l  Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]

l  Frequency-tuned salient region detection [2] [Project]

l  Saliency detection using maximum symmetric surround [3] [Project]

l  Attention via Information Maximization [4] [Matlab code]

l  Context-aware saliency detection [5] [Matlab code]

l  Graph-based visual saliency [6] [Matlab code]

l  Saliency detection: A spectral residual approach. [7] [Matlab code]

l  Segmenting salient objects from images and videos. [8] [Matlab code]

l  Saliency Using Natural statistics. [9] [Matlab code]

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

l  Learning to Predict Where Humans Look [11] [Project]

l  Global Contrast based Salient Region Detection [12] [Project]

l  Bayesian Saliency via Low and Mid Level Cues[Project]

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

l  Saliency Detection: A Spectral Residual Approach[Code]

 

五、图像分类、聚类Image Classification, Clustering

l  Pyramid Match [1] [Project]

l  Spatial Pyramid Matching [2] [Code]

l  Locality-constrained Linear Coding [3] [Project] [Matlab code]

l  Sparse Coding [4] [Project] [Matlab code]

l  Texture Classification [5] [Project]

l  Multiple Kernels for Image Classification [6] [Project]

l  Feature Combination [7] [Project]

l  SuperParsing [Code]

l  Large Scale Correlation Clustering Optimization[Matlab code]

l  Detecting and Sketching the Common[Project]

l  Self-Tuning Spectral Clustering[Project][Code]

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

l  Filters for Texture Classification[Project]

l  Multiple Kernel Learning for Image Classification[Project]

l  SLIC Superpixels[Project]

 

六、抠图Image Matting

l  A Closed Form Solution to Natural Image Matting [Code]

l  Spectral Matting [Project]

l  Learning-based Matting [Code]

 

七、目标跟踪Object Tracking：

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

l  Object Tracking via Partial Least Squares Analysis[Paper][Code]

l  Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]

l  Online Visual Tracking with Histograms and Articulating Blocks[Project]

l  Incremental Learning for Robust Visual Tracking[Project]

l  Real-time Compressive Tracking[Project]

l  Robust Object Tracking via Sparsity-based Collaborative Model[Project]

l  Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]

l  Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]

l  Superpixel Tracking[Project]

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

l  Online Multiple Support Instance Tracking [Paper][Code]

l  Visual Tracking with Online Multiple Instance Learning[Project]

l  Object detection and recognition[Project]

l  Compressive Sensing Resources[Project]

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

l  Tracking-Learning-Detection[Project][OpenTLD/C++ Code]

l  the HandVu：vision-based hand gesture interface[Project]

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

 

八、Kinect：

l  Kinect toolbox[Project]

l  OpenNI[Project]

l  zouxy09 CSDN Blog[Resource]

l  FingerTracker 手指跟踪[code]

 

九、3D相关：

l  3D Reconstruction of a Moving Object[Paper] [Code]

l  Shape From Shading Using Linear Approximation[Code]

l  Combining Shape from Shading and Stereo Depth Maps[Project][Code]

l  Shape from Shading: A Survey[Paper][Code]

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

l  Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]

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

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

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

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

 

十、机器学习算法：

l  Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]

l  Random Sampling[code]

l  Probabilistic Latent Semantic Analysis (pLSA)[Code]

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

l  Fast Intersection / Additive Kernel SVMs[Project]

l  SVM[Code]

l  Ensemble learning[Project]

l  Deep Learning[Net]

l  Deep Learning Methods for Vision[Project]

l  Neural Network for Recognition of Handwritten Digits[Project]

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

l  THE MNIST DATABASE of handwritten digits[Project]

l  Ersatz：deep neural networks in the cloud[Project]

l  Deep Learning [Project]

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

l  Weka 3: Data Mining Software in Java[Project]

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

l  CNN - Convolutional neural network class[Matlab Tool]

l  Yann LeCun's Publications[Wedsite]

l  LeNet-5, convolutional neural networks[Project]

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

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

l  Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]

l  Sparse coding simulation software[Project]

l  Visual Recognition and Machine Learning Summer School[Software]

 

十一、目标、行为识别Object, Action Recognition：

l  Action Recognition by Dense Trajectories[Project][Code]

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

l  Recognition Using Regions[Paper][Code]

l  2D Articulated Human Pose Estimation[Project]

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

l  Estimating Human Pose from Occluded Images[Paper][Code]

l  Quasi-dense wide baseline matching[Project]

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

l  Real Time Head Pose Estimation with Random Regression Forests[Project]

l  2D Action Recognition Serves 3D Human Pose Estimation[Project]

l  A Hough Transform-Based Voting Framework for Action Recognition[Project]

l  Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]

l  2D articulated human pose estimation software[Project]

l  Learning and detecting shape models [code]

l  Progressive Search Space Reduction for Human Pose Estimation[Project]

l  Learning Non-Rigid 3D Shape from 2D Motion[Project]

 

十二、图像处理：

l  Distance Transforms of Sampled Functions[Project]

l  The Computer Vision Homepage[Project]

l  Efficient appearance distances between windows[code]

l  Image Exploration algorithm[code]

l  Motion Magnification 运动放大 [Project]

l  Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]

l  A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

 

十三、一些实用工具：

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

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

l  Fast Artificial Neural Network Library[Project]

 

十四、人手及指尖检测与识别：

l  finger-detection-and-gesture-recognition [Code]

l  Hand and Finger Detection using JavaCV[Project]

l  Hand and fingers detection[Code]

 

十五、场景解释：

l  Nonparametric Scene Parsing via Label Transfer [Project]

 

十六、光流Optical flow：

l  High accuracy optical flow using a theory for warping [Project]

l  Dense Trajectories Video Description [Project]

l  SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]

l  KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]

l  Tracking Cars Using Optical Flow[Project]

l  Secrets of optical flow estimation and their principles[Project]

l  implmentation of the Black and Anandan dense optical flow method[Project]

l  Optical Flow Computation[Project]

l  Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]

l  A Database and Evaluation Methodology for Optical Flow[Project]

l  optical flow relative[Project]

l  Robust Optical Flow Estimation [Project]

l  optical flow[Project]

 

十七、图像检索Image Retrieval：

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

 

十八、马尔科夫随机场Markov Random Fields：

l  Markov Random Fields for Super-Resolution [Project]

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

 

十九、运动检测Motion detection：

l  Moving Object Extraction, Using Models or Analysis of Regions [Project]

l  Background Subtraction: Experiments and Improvements for ViBe [Project]

l  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]

l  changedetection.net: A new change detection benchmark dataset[Project]

l  ViBe - a powerful technique for background detection and subtraction in video sequences[Project]

l  Background Subtraction Program[Project]

l  Motion Detection Algorithms[Project]

l  Stuttgart Artificial Background Subtraction Dataset[Project]

l  Object Detection, Motion Estimation, and Tracking[Project]

 

Feature Detection and Description

General Libraries: 

l  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

l  OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

 

Fast Keypoint Detectors for Real-time Applications: 

l  FAST – High-speed corner detector implementation for a wide variety of platforms

l  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: 

l  BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)

l  ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)

l  BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)

l  FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)

 

SIFT and SURF Implementations: 

l  SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT

l  SURF: Herbert Bay’s code, OpenCV, GPU-SURF

 

Other Local Feature Detectors and Descriptors: 

l  VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.

l  LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).

l  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: 

l  GIST – Matlab code for the GIST descriptor

l  CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)

 

Feature Coding and Pooling 

l  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.

l  Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)

 

Convolutional Nets and Deep Learning 

l  EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.

l  Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.

l  Deep Learning - Various links for deep learning software.

 

Part-Based Models 

l  Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)

l  Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.

l  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).

l  Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).

l  Poselets – C++ and Matlab versions for object detection based on poselets.

l  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 

l  Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).

l  Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank

l  Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

 

Large-Scale Learning 

l  Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).

l  LIBLINEAR – Library for large-scale linear SVM classification.

l  VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.

 

Fast Indexing and Image Retrieval 

l  FLANN – Library for performing fast approximate nearest neighbor.

l  Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).

l  ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).

l  INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

 

Object Detection 

l  See Part-based Models and Convolutional Nets above.

l  Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).

l  Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.

l  OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.

l  Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

 

3D Recognition 

l  Point-Cloud Library – Library for 3D image and point cloud processing.

 

Action Recognition 

l  ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).

l  STIP Features – software for computing space-time interest point descriptors

l  Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)

l  Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

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Datasets

 

Attributes 

l  Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.

l  aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.

l  FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.

l  PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.

l  LFW – 13,233 face images of 5,749 people with 73 attribute classifier outputs.

l  Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.

l  SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.

l  ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.

l  Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.

l  Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.

 

Fine-grained Visual Categorization 

l  Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.

l  Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.

l  Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.

l  Leeds Butterfly Dataset – 832 images of 10 species of butterflies.

l  Oxford Flower Dataset – Hundreds of flower categories.

 

Face Detection 

l  FDDB – UMass face detection dataset and benchmark (5,000+ faces)

l  CMU/MIT – Classical face detection dataset.

 

Face Recognition 

l  Face Recognition Homepage – Large collection of face recognition datasets.

l  LFW – UMass unconstrained face recognition dataset (13,000+ face images).

l  NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.

l  CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.

l  FERET – Classical face recognition dataset.

l  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.

l  SCFace – Low-resolution face dataset captured from surveillance cameras.

 

Handwritten Digits 

l  MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.

 

Pedestrian Detection

l  Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.

l  INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.

l  ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.

l  TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.

l  PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.

l  USC Pedestrian Dataset – Small dataset captured from surveillance cameras.

 

Generic Object Recognition 

l  ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.

l  Tiny Images – 80 million 32x32 low resolution images.

l  Pascal VOC – One of the most influential visual recognition datasets.

l  Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.

l  MIT LabelMe – Online annotation tool for building computer vision databases.

 

Scene Recognition

l  MIT SUN Dataset – MIT scene understanding dataset.

l  UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.

 

Feature Detection and Description 

l  VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarks for an evaluation framework.

 

Action Recognition

l  Benchmarking Activity Recognition – CVPR 2012 tutorial covering various datasets for action recognition.

 

RGBD Recognition 

l  RGB-D Object Dataset – Dataset containing 300 common household objects

 

Reference:

[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html

 

转自：http://www.cnblogs.com/einyboy/p/3594432.html