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機器視覺開原始碼

一、特徵提取Feature Extraction:

  • PCA-SIFT [2] [Project]
  • Affine-SIFT [3] [Project]
  • Affine Covariant Features [5] [Oxford project]
  • Geometric Blur [7] [Code]
  • Local Self-Similarity Descriptor [8] [Oxford implementation]
  • Global and Efficient Self-Similarity [9] [Code]
  • 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]
  • Online Selection of Discriminative Tracking Features[Project]

二、影象分割Image Segmentation:

  • Normalized Cut [1] [Matlab code]
  • Gerg Mori’ Superpixel code [2] [Matlab code]
  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
  • OWT-UCM Hierarchical Segmentation [5] [Resources]
  • 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、目標檢測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、顯著性檢測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、影象分類、聚類Image Classification, Clustering

  • Pyramid Match [1] [Project]
  • Spatial Pyramid Matching [2] [Code]
  • Locality-constrained Linear Coding [3] [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、摳圖Image Matting

  • A Closed Form Solution to Natural Image Matting [Code]
  • Spectral Matting [Project]
  • Learning-based Matting [Code]

七、目標跟蹤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]
  • the HandVu:vision-based hand gesture interface[Project]
  • Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]

八、Kinect:

九、3D相關:

  • 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、機器學習演算法

  • 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 (餘凱)[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、目標、行為識別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、影象處理:

  • Distance Transforms of Sampled Functions[Project]
  • The Computer Vision Homepage[Project]
  • Efficient appearance distances between windows[code]
  • Image Exploration algorithm[code]
  • Motion Magnification 運動放大 [Project]
  • Bilateral Filtering for Gray and Color Images 雙邊濾波器 [Project]
  • A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

十三、一些實用工具:

  • 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、人手及指尖檢測與識別:

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

十五、場景解釋:

  • 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、影象檢索Image Retrieval

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

十八、馬爾科夫隨機場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、運動檢測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