• 机器视觉开源代码集合（转载）

         本文属于转载内容，也在笔者另外一个博客（http://blog.csdn.net/qq_37608890/article/details/79352633）发布。

    来源：http://blog.csdn.net/flyingpig851334799/article/details/47449847。

    一、特征提取Feature Extraction：

    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：

    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: 

    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 

    Attributes and Semantic Features 

    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 

    3D Recognition 

    Action Recognition 

    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 

    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

    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

    Feature Detection and Description 

    Action Recognition

    RGBD Recognition 

    Reference:

    [1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html
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  • 原文地址：https://www.cnblogs.com/georgeli/p/8461511.html
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