机器视觉开源代码集合

一、特征提取Feature Extraction：

•SIFT 1 [Demo program][SIFT Library] [VLFeat]
•PCA-SIFT [2] [Project]
•Affine-SIFT [3] [Project]
•SURF [4] [OpenSURF] [Matlab Wrapper]
•Affine Covariant Features [5] [Oxford project]
•MSER [6] [Oxford project] [VLFeat]
•Geometric Blur [7] [Code]
•Local Self-Similarity Descriptor [8] [Oxford implementation]
•Global and Efficient Self-Similarity [9] [Code]
•Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]
•GIST [11] [Project]
•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]
•HOG[Project]
•Online Selection of Discriminative Tracking Features[Project]

二、图像分割Image Segmentation：
•Normalized Cut 1 [Matlab code]
•Gerg Mori’ Superpixel code [2] [Matlab code]
•Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
•Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
•OWT-UCM Hierarchical Segmentation [5] [Resources]
•Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
•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、目标检测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、显著性检测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、图像分类、聚类Image Classification, Clustering
•Pyramid Match 1 [Project]
•Spatial Pyramid Matching [2] [Code]
•Locality-constrained Linear Coding [3] [Project] [Matlab code]
•Sparse Coding [4] [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、抠图Image Matting
•A Closed Form Solution to Natural Image Matting [Code]
•Spectral Matting [Project]
•Learning-based Matting [Code]

七、目标跟踪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]
•Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
•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]
•OpenNI[Project]
•zouxy09 CSDN Blog[Resource]
•FingerTracker 手指跟踪[code]

九、3D相关：
•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、机器学习算法：
•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 (余凯)[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、目标、行为识别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、图像处理：
•Distance Transforms of Sampled Functions[Project]
•The Computer Vision Homepage[Project]
•Efficient appearance distances between windows[code]
•Image Exploration algorithm[code]
•Motion Magnification 运动放大 [Project]
•Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]
•A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

十三、一些实用工具：
•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、人手及指尖检测与识别：
•finger-detection-and-gesture-recognition [Code]
•Hand and Finger Detection using JavaCV[Project]
•Hand and fingers detection[Code]

十五、场景解释：
•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、图像检索Image Retrieval：
•Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]

十八、马尔科夫随机场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、运动检测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:
•SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT
•SURF: Herbert Bay’s code, OpenCV, GPU-SURF

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, NIPS 2011, CVPR 2012).

Large-Scale Learning
•Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
•LIBLINEAR – Library for large-scale linear SVM classification.
•VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.

Fast Indexing and Image Retrieval
•FLANN – Library for performing fast approximate nearest neighbor.
•Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
•ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).
•INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

Object Detection
•See Part-based Models and Convolutional Nets above.
•Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).
•Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.
•OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.
•Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

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

Action Recognition
•ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).
•STIP Features – software for computing space-time interest point descriptors
•Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)
•Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

Datasets

Attributes
•Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
•aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
•FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
•PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
•LFW – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
•Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.
•SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
•ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
•Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.
•Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.

Fine-grained Visual Categorization
•Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.
•Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.
•Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.
•Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
•Oxford Flower Dataset – Hundreds of flower categories.

Face Detection
•FDDB – UMass face detection dataset and benchmark (5,000+ faces)
•CMU/MIT – Classical face detection dataset.

Face Recognition
•Face Recognition Homepage – Large collection of face recognition datasets.
•LFW – UMass unconstrained face recognition dataset (13,000+ face images).
•NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
•CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
•FERET – Classical face recognition dataset.
•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.
•SCFace – Low-resolution face dataset captured from surveillance cameras.

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

Pedestrian Detection
•Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.
•INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.
•ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.
•TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.
•PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.
•USC Pedestrian Dataset – Small dataset captured from surveillance cameras.

Generic Object Recognition
•ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
•Tiny Images – 80 million 32x32 low resolution images.
•Pascal VOC – One of the most influential visual recognition datasets.
•Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.
•MIT LabelMe – Online annotation tool for building computer vision databases.

Scene Recognition
•MIT SUN Dataset – MIT scene understanding dataset.
•UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.

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

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

RGBD Recognition
•RGB-D Object Dataset – Dataset containing 300 common household objects

Reference: