计算机视觉
图像处理

机器视觉开源代码集合

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 DescriptionGeneral 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 


DatasetsAttributes 

  • 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 32×32 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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Related Courses

Deep Residual Networks

Deep Residual Learning for Image Recognition
https://github.com/KaimingHe/deep-residual-networks

Identity Mappings in Deep Residual Networks (by Kaiming He)

arxiv: http://arxiv.org/abs/1603.05027
github: https://github.com/KaimingHe/resnet-1k-layers
github: https://github.com/bazilas/matconvnet-ResNet
github: https://github.com/FlorianMuellerklein/Identity-Mapping-ResNet-Lasagne

Wide Residual Networks

arxiv: http://arxiv.org/abs/1605.07146
github: https://github.com/szagoruyko/wide-residual-networks
github: https://github.com/asmith26/wide_resnets_keras

Inception-V4, Inception-Resnet And The Impact Of Residual Connections On Learning (Workshop track – ICLR 2016)

intro: “achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge”
arxiv: http://arxiv.org/abs/1602.07261
paper: http://beta.openreview.net/pdf?id=q7kqBkL33f8LEkD3t7X9
github: https://github.com/lim0606/torch-inception-resnet-v2

Object detection
Object detection via a multi-region & semantic segmentation-aware CNN model
https://github.com/gidariss/mrcnn-object-detection

DeepBox: Learning Objectness with Convolutional Networks ICCV2015
proposal re-ranker
https://github.com/weichengkuo/DeepBox

Object-Proposal Evaluation Protocol is ‘Gameable’ 好多 Proposal 代码
https://github.com/batra-mlp-lab/object-proposals

Fast R-CNN
https://github.com/rbgirshick/fast-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/ShaoqingRen/faster_rcnn MATLAB
https://github.com/rbgirshick/py-faster-rcnn Python

YOLO : Real-Time Object Detection
http://pjreddie.com/darknet/yolo/
https://github.com/pjreddie/darknet

SSD: Single Shot MultiBox Detector 比Faster R-CNN又快又好啊!
https://github.com/weiliu89/caffe/tree/ssd

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
https://github.com/zhaoweicai/mscnn

Image Question Answering
Stacked Attention Networks for Image Question Answering CVPR2016
https://github.com/zcyang/imageqa-san

Image Question Answering using Convolutional Neural Networ with Dynamic Parameter Prediction CVPR2016

项目网页
http://cvlab.postech.ac.kr/research/dppnet/
开源代码
https://github.com/HyeonwooNoh/DPPnet

场景识别:
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust
Semantic Pixel-Wise Labelling
https://github.com/alexgkendall/caffe-segnet

Tracking:
Learning to Track: Online Multi-Object Tracking by Decision Making ICCV2015
使用 Markov Decision Processes 做跟踪,速度可能比较慢,效果应该还可以
https://github.com/yuxng/MDP_Tracking

Fully-Convolutional Siamese Networks for Object Tracking
http://www.robots.ox.ac.uk/~luca/siamese-fc.html

Car detection:
Integrating Context and Occlusion for Car Detection by Hierarchical And-or Model ECCV2014
http://www.stat.ucla.edu/~boli/projects/context_occlusion/context_occlusion.html

Face detection

人脸检测2015进展:http://www.cvrobot.net/latest-progress-in-face-detection-2015/

Face detection without bells and whistles
project:http://markusmathias.bitbucket.org/2014_eccv_face_detection/
Code:https://bitbucket.org/rodrigob/doppia
Talk: http://videolectures.net/eccv2014_mathias_face_detection/

From Facial Parts Responses to Face Detection: A Deep Learning Approach ICCV2015 email to get code and model
http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html

A Fast and Accurate Unconstrained Face Detector 2015 PAMI
简单 快速 有效
http://www.cbsr.ia.ac.cn/users/scliao/projects/npdface/

Face Alignment
Face Alignment by Coarse-to-Fine Shape Searching
http://mmlab.ie.cuhk.edu.hk/projects/CFSS.html

High-Fidelity Pose and Expression Normalization for Face Recognition
in the Wild
http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/HPEN/main.htm

Face Recognition
Deep face recognition
http://www.robots.ox.ac.uk/~vgg/software/vgg_face/

Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
http://www.openu.ac.il/home/hassner/projects/augmented_faces/

Person Re-identification :

Person Re-identification Results
http://www.ssig.dcc.ufmg.br/reid-results/#ref35VIPER

Learning a Discriminative Null Space for Person Re-identification
code http://www.eecs.qmul.ac.uk/~lz/

Query-Adaptive Late Fusion for Image Search and Person Re-identification
CVPR2015
http://www.liangzheng.com.cn/Project/project_fusion.html

Efficient Person Re-identification by Hybrid Spatiogram and Covariance Descriptor CVPR2015 Workshops
https://github.com/Myles-ZMY/HSCD

Person Re-Identification by Iterative Re-Weighted Sparse Ranking PAMI 2015
http://www.micc.unifi.it/masi/code/isr-re-id/ 没有特征提取代码

Person re-identification by Local Maximal Occurrence representation and metric learning CVPR2015
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/

Head detection
Context-aware CNNs for person head detection
Matlab code & dataset avaiable
http://www.di.ens.fr/willow/research/headdetection/

Pedestrian detection

Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning PAMI 2015
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features ECCV2014
https://github.com/chhshen/pedestrian-detection

Is Faster R-CNN Doing Well for Pedestrian Detection
Matlab 代码 :https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

Deep Learning
Deeply Learned Attributes for Crowded Scene Understanding
https://github.com/amandajshao/www_deep_crowd
http://www.ee.cuhk.edu.hk/~jshao/WWWCrowdDataset.html

Quantized Convolutional Neural Networks for Mobile Devices
https://github.com/jiaxiang-wu/quantized-cnn

Human Pose Estimation
DeepPose: Human Pose Estimation via Deep Neural Networks, CVPR2014
https://github.com/mitmul/deeppose not official implementation

Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations NIPS 2014
http://www.stat.ucla.edu/~xianjie.chen/projects/pose_estimation/pose_estimation.html

Learning Human Pose Estimation Features with Convolutional Networks
https://github.com/stencilman/deep_nets_iclr04

Flowing ConvNets for Human Pose Estimation in Videos
http://www.robots.ox.ac.uk/~vgg/software/cnn_heatmap/

杂项
Unsupervised Learning of Visual Representations using Videos 很有前途啊!
https://github.com/xiaolonw/caffe-video_triplet

Learning Deep Representations of Fine-Grained Visual Descriptions
https://github.com/reedscot/cvpr2016

Fast Detection of Curved Edges at Low SNR
http://www.wisdom.weizmann.ac.il/~yehonato/projectPage.html

Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance

code: https://medusa.fit.vutbr.cz/traffic/research-topics/fine-grained-vehicle-recognition/unsupervised-processing-of-vehicle-appearance-for-automatic-understanding-in-traffic-surveillance/

Image Retrieval
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
https://github.com/kevinlin311tw/cvpr16-deepbit

Deep Supervised Hashing for Fast Image Retrieval
https://github.com/lhmRyan/deep-supervised-hashing-DSH

Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification
https://github.com/ruixuejianfei/BitScalableDeepHash

数据库
MPII Human Pose Dataset  http://human-pose.mpi-inf.mpg.de/#overview

WIDER FACE: A Face Detection Benchmark 数据库 http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/

DPM
将voc-release4.0.1 linux 转到windows
http://blog.csdn.net/masibuaa/article/details/17577195

开源车牌识别代码
支持美国和欧洲车牌
http://www.openalpr.com

文字识别
https://github.com/MichalBusta/FASText
FASText: Efficient Unconstrained Scene Text Detector

模板匹配
FAsT-Match: Fast Affine Template Matching
Best-Buddies Similarity for Robust Template Matching
siftdemoV4

 

机器视觉开源处理库:

通用库/General Library

图像,视频IO/Image, Video IO

AR相关/Augmented Reality

基于Marker的AR库

  • ARToolKitPlus   ARToolKit的增强版。实现了更好的姿态估计算法。
  • PTAM   实时的跟踪、SLAM、AR库。无需Marker,模板,内置传感器等。
  • BazAR   基于特征点检测和识别的AR库。

局部不变特征/Local Invariant Feature

  • VLFeat   目前最好的Sift开源实现。同时包含了KD-tree,KD-Forest,BoW实现。
  • Ferns   基于Naive Bayesian Bundle的特征点识别。高速,但占用内存高。

目标检测/Object Detection

(近似)最近邻/ANN

  • FLANN    目前最完整的(近似)最近邻开源库。不但实现了一系列查找算法,还包含了一种自动选取最快算法的机制。
  • ANN   另外一个近似最近邻库。

SLAM & SFM

  • SceneLib [LGPL]   monoSLAM库。由Androw Davison开发。

图像分割/Segmentation

  • SLIC Super Pixel   使用Simple Linear Iterative Clustering产生指定数目,近似均匀分布的Super Pixel。

目标跟踪/Tracking

  • TLD   基于Online Random Forest的目标跟踪算法。
  • KLT   Kanade-Lucas-Tracker

直线检测/Line Detection

  • DSCC   基于联通域连接的直线检测算法。
  • LSD [GPL]  基于梯度的,局部直线段检测算子。

指纹/Finger Print

  • pHash [GPL]   基于感知的多媒体文件Hash算法。(提取,对比图像、视频、音频的指纹)

视觉显著性/Visual Salience

FFT/DWT

  • FFTW [GPL]   最快,最好的开源FFT。
  • FFTReal [WTFPL]   轻量级的FFT实现。许可证是亮点。

音频处理/Audio processing

  • STK [Free]  音频处理,音频合成。
  • libsamplerate [GPL ]   音频重采样。小波变换快速小波变换(FWT)

BRIEF: Binary Robust Independent Elementary Feature 一个很好的局部特征描述子,里面有FAST corner + BRIEF实现特征点匹配的DEMO:http://cvlab.epfl.ch/software/brief/http://code.google.com/p/javacv
Java打包的OpenCV, FFmpeg, libdc1394, PGR FlyCapture, OpenKinect, videoInput, and ARToolKitPlus库。可以放在Android上用~libHIK,HIK SVM,计算HIK SVM跟Centrist的Lib。http://c2inet.sce.ntu.edu.sg/Jianxin/projects/libHIK/libHIK.htm一组视觉显著性检测代码的链接:http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/

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